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Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential


Lei Zhang, Gábor Csányi, Erik van der Giessen, Francesco Maresca

  • Existing, classical interatomic potentials for bcc iron predict contradicting crack-tip mechanisms (i.e. cleavage, dislocation emission, phase transition) for the same crack systems, thus leaving the crack propagation mechanism in bcc iron unclear. In this work, we develop a Gaussian approximation potential (GAP) by extending a DFT database for ferromagnetic bcc iron to include highly distorted primitive bcc cells and surface separation, along with small crack-tip configurations that are identified by means of a fully automated active learning workflow. Our GAP (referred to as Fe-GAP22) predicts crack propagation within 8 meV/atom accuracy. The fully automated, active learning workflow is made publicly available on GitHub. With the newly developed Fe-GAP22, we find that in absence of other defects around the crack tip (e.g. nanovoids, dislocations), the static (T=0K) crack-tip mechanism is cleavage, thus settling the contradictions in the literature. Our work also highlights the ...

Latest version: v1
Publication date: Aug 11, 2022

Gas adsorption and process performance data for MOFs


Kevin Maik Jablonka, Andrew S. Rosen, Berend Smit

  • Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.

Latest version: v1
Publication date: Aug 11, 2022

Experimental and theoretical study of stable and metastable phases in sputtered CuInS2


Jes Larsen, Kostiantyn Sopiha, Clas Persson, Charlotte Platzer-Björkman, Marika Edoff

  • The chalcopyrite Cu(In,Ga)S2 has gained renewed interest in recent years due to its potential application in tandem solar cells. In this contribution, a combined theoretical and experimental approach is applied to investigate stable and metastable phases forming in sputtered CuInS2 (CIS) thin films. Ab initio calculations are performed to obtain formation energies, X-ray diffraction patterns, and Raman spectra of various CIS polytypes and related compounds. Multiple low-energy CIS structures with zinc-blende and wurtzite-derived lattices are identified and their XRD/Raman patterns are shown to contain many overlapping features, which could lead to misidentification unless the techniques are duly combined and analyzed. The results are verified against experimental XRD/Raman spectra measured on a series of CIS films with different compositions and treated at different temperatures, revealing the formation of several CIS polymorphs and secondary phases. The characteristic features ...

Latest version: v2
Publication date: Aug 11, 2022

AI powered, automated discovery of polymer membranes for carbon capture


Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto, Toshiyuki Hama, Rodrigo F. Neumann, Binquan Luan, Seiji Takeda, Lisa Hamada, Mathias B. Steiner

  • Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.

Latest version: v5
Publication date: Jul 27, 2022

Koopmans spectral functionals: an open-source periodic-boundary implementation


Nicola Colonna, Riccardo De Gennaro, Edward Linscott, Nicola Marzari

  • Koopmans' spectral functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. In addition to delivering improved scaling with system size, the present development makes the calculation of band structures with Koopmans functionals straightforward. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.

Latest version: v2
Publication date: Jul 22, 2022

Temperature dependent properties of the aqueous electron


Jinggang Lan, Vladimir Rybkin, Alfredo Pasquarello

  • The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.

Latest version: v1
Publication date: Jul 20, 2022

Shape-controlled pathways in the hydrogen production from ethanol steam reforming over ceria nanoparticles


Julia Vecchietti, Patricia Pérez-Bailac, Pablo G. Lustemberg, Esteban L. Fornero, Laura Pascual, Marta Bosco, Arturo Martínez-Arias, M. Veronica Ganduglia-Pirovano, Adrian L. Bonivardi

  • The ethanol surface reaction over CeO₂ nanooctahedra (NO) and nanocubes (NC), which mainly expose (111) and (100) surfaces, respectively, was studied by means of infrared spectroscopy (TPSR-IR), mass spectrometry (TPSR-MS) and density functional theory (DFT) calculations. TPSR-MS results show that the production of H₂ is 2.4 times higher on CeO₂ -NC than on -NO, which is rationalized starting from the different types of adsorbed ethoxy species controlled by the shape of the ceria particles. Over the CeO₂(111) surface, monodentate type I and II ethoxy species with the alkyl chain perpendicular or parallel to the surface, respectively, were identified. Whereas on the CeO₂(100) surface, bidentate and monodentate type III ethoxy species on the checkerboard O-terminated and on a pyramid of the reconstructed (100) surface, respectively, are found. The more labile surface ethoxy species on each ceria nanoshape, which are the monodentate type I or III ethoxy on CeO₂ -NO and -NC, ...

Latest version: v1
Publication date: Jul 19, 2022

Reactivity of layered manganese oxide toward water oxidation under alkaline conditions in presence and in absence of iron


Ivan Kondov, Matthias Vandichel

  • This dataset includes the computational workflows of a density functional theory based study of the oxygen evolution reaction (OER) on a manganese oxide catalyst in presence and absence of iron dopant. The thermodynamic OER overpotential has been computed by using a surface slab model based on a layered birnessite bulk structure of MnO₂ considering supercells with two and four MnO₂ units and by varying the intercalation with KOH, the amount of Fe dopant and the dopant positions. In addition, the dependence of the oxidation state of the active site atoms (either Mn or Fe) on the directly bound OER intermediate species, has been investigated. The results suggest a decrease of up to 310 mV in the thermodynamic OER overpotential upon doping the considered model structures with Fe that is consistent with the experimentally measured total overpotential decrease of 190 mV.

Latest version: v1
Publication date: Jul 19, 2022

Oxygen evolution reaction by a palladium foil in the presence of iron


Ivan Kondov, Matthias Vandichel

  • This dataset includes the full computational workflows of a density functional theory based thermodynamics model for the overpotential of the oxygen evolution reaction (OER) on an oxidized palladium surface. The model assumes an oxygen bridge vacancy as an active site on the 110 surface of the tetragonal PdO2 (rutile type structure). The critical OER potential has been computed with variation of the Fe modifyer type, either dopant or adsorbate, and modifyer position. Furthermore, an alternative bifunctional pathway of OER has been considered by adding an H atom to an auxiliary O-bridge site from which the proton−electron pair for second OER reaction step is released rather than from the hydroxylated active site. The computed OER overpotential on the Fe-free surface via this bifunctional route is 0.42 V. A substitution of Pd with Fe directly at this active site further reduces the calculated OER overpotential, over the same route, to 0.35 V. This 70 mV decrease in overpotential is ...

Latest version: v1
Publication date: Jul 19, 2022

Oxygen evolution and reduction on Fe‑doped NiOOH


Matthias Vandichel, Kari Laasonen, Ivan Kondov

  • This dataset includes the full computational workflows of a proof-of-concept study of various possible mechanisms (standard and bifunctional ones) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) on exfoliated NiOOH as electrocatalyst, including active edge sites (M5) and hydrogen acceptor sites in the same model system. Furthermore, explicit water is included in the model to describe the equilibration of the M-OOH species to M-OOH/eq, a crucial step that enables a bifunctional route to be operative. Additionally, different single Fe-dopant positions (M1, M2, M3, M4, M5, M6 and M7) are considered and four different reaction schemes (S1, S2, S3 and S4) are studied for the OER and the reverse ORR process. The results are relevant in alkaline conditions, where the studied model systems are stable. Certain Fe-dopant positions result in active Ni-edge sites with very low overpotentials provided that water is present within the model system.

Latest version: v1
Publication date: Jul 18, 2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt


Jonathan Vandermause, Yu Xie, Jin Soo Lim, Cameron Owen, Boris Kozinsky

  • Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

Latest version: v1
Publication date: Jul 18, 2022

Artificial intelligence enables mobile soil analysis for sustainable agriculture


Ademir Ferreira da Silva, Ricardo Luis Ohta, Jaione Tirapu Azpiroz, Matheus Esteves Fereira, Daniel Vitor Marçal, André Botelho, Tulio Coppola, Allysson Flavio Melo de Oliveira, Murilo Bettarello, Lauren Schneider, Rodrigo Vilaça, Noorunisha Abdool, Pedro Augusto Malanga, Vanderlei Junior, Wellington Furlaneti, Mathias Steiner

  • For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis ...

Latest version: v4
Publication date: Jul 18, 2022

Bloch's theorem in orbital-density-dependent functionals: Band structures from Koopmans spectral functionals


Riccardo De Gennaro, Nicola Colonna, Edward Linscott, Nicola Marzari

  • Koopmans-compliant functionals provide an orbital-density-dependent framework for an accurate evaluation of spectral properties; they are obtained by imposing a generalized piecewise-linearity condition on the total energy of the system with respect to the occupation of any orbital. In crystalline materials, due to the orbital-density-dependent nature of the functionals, minimization of the total energy to a ground state provides a set of minimizing variational orbitals that are localized and thus break the periodicity of the underlying lattice. Despite this, we show that Bloch symmetry can be preserved and it is possible to describe the electronic states with a band-structure picture, thanks to the Wannier-like character of the variational orbitals. We also present a method to unfold and interpolate the electronic bands from supercell (Γ-point) calculations, which enables us to calculate full band structures with Koopmans-compliant functionals. The results obtained for a set of ...

Latest version: v2
Publication date: Jul 08, 2022

turboMagnon - A code for the simulation of spin-wave spectra using Liouville-Lanczos approach to time-dependent density-functional perturbation theory


Tommaso Gorni, Oscar Baseggio, Pietro Delugas, Stefano Baroni, Iurii Timrov

  • We introduce turboMagnon, an implementation of the Liouville-Lanczos approach to linearized time-dependent density-functional theory, designed to simulate spin-wave spectra in solid-state materials. The code is based on the noncollinear spin-polarized framework and the self-consistent inclusion of spin-orbit coupling that allow to model complex magnetic excitations. The spin susceptibility matrix is computed using the Lanczos recursion algorithm that is implemented in two flavors - the non-Hermitian and the pseudo-Hermitian one. turboMagnon is open-source software distributed under the terms of the GPL as a component of QE. As with other components, turboMagnon is optimized to run on massively parallel architectures using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing magnon dispersions for the CrI₃ monolayer, and the importance of the spin-orbit coupling is discussed.

Latest version: v1
Publication date: Jun 29, 2022

Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling


Lorenzo Gigli, Max Veit, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, Michele Ceriotti

  • Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. ...

Latest version: v1
Publication date: Jun 29, 2022

In situ spectroelectrochemical probing of CO redox landscape on copper single-crystal surfaces


Feng Shao, Jun Kit Wong, Qi Hang Low, Marcella Iannuzzi, Jingguo Li, Jinggang Lan

  • Electrochemical reduction of CO(2) to value-added chemicals and fuels is a promising strategy to sustain pressing renewable energy demands and address climate change issues. Direct observation of reaction intermediates during the CO(2) reduction reaction will contribute to mechanistic understandings and thus promote the design of catalysts with the desired activity, selectivity, and stability. Herein, we combined in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy and ab initio molecular dynamics calculations to investigate the CORR process on Cu single-crystal surfaces in various electrolytes. Competing redox pathways and coexistent intermediates of CO adsorption dimerization, oxidation, and hydrogenation, as well as Cu-Oad/Cu-OHad species at Cu-electrolyte interfaces, were simultaneously identified using in situ spectroscopy and further confirmed with isotope-labeling experiments. With AIMD simulations, we report accurate vibrational frequency ...

Latest version: v1
Publication date: Jun 28, 2022

Machine-learning accelerated identification of exfoliable two-dimensional materials


Mohammad Tohidi Vahdat, Kumar Agrawal Varoon, Giovanni Pizzi

  • Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we ...

Latest version: v1
Publication date: Jun 24, 2022

The Materials Cloud 2D database (MC2D)


Davide Campi, Nicolas Mounet, Marco Gibertini, Giovanni Pizzi, Nicola Marzari

  • Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the ...

Latest version: v1
Publication date: Jun 24, 2022

Pivotal role of intersite Hubbard interactions in Fe-doped α-MnO₂


Ruchika Mahajan, Arti Kashyap, Iurii Timrov

  • We present a first-principles investigation of the structural, electronic, and magnetic properties of the pristine and Fe-doped α-MnO₂ using density-functional theory with extended Hubbard functionals. The onsite U and intersite V Hubbard parameters are determined from first principles and self-consistently using density-functional perturbation theory in the basis of Löwdin-orthogonalized atomic orbitals. First, we analyze the pristine α-MnO₂ and show that the C2-AFM spin configuration is the most energetically favorable, in agreement with the experimentally observed antiferromagnetic state. For the Fe-doped α-MnO₂ two types of doping are considered: Fe insertion in the 2 × 2 tunnels and partial substitution of Fe for Mn. The calculated formation energies show that the experimentally observed Fe insertion is energetically favorable only when intersite Hubbard interactions are taken into account. Moreover, we find that both types of doping preserve the C2-AFM spin configuration of ...

Latest version: v2
Publication date: Jun 23, 2022

Towards a robust evaluation of nanoporous materials for carbon capture applications


Elias Moubarak, Seyed Mohamad Moosavi, Charithea Charalambous, Susana Garcia, Berend Smit

  • In this paper, we present a workflow that is designed to work without manual intervention to efficiently predict, by using molecular simulations, the thermodynamic data that is needed to design a carbon capture process. We developed a procedure that does not rely on fitting of the adsorption isotherms. From molecular simulations, we can obtain accurate data for both, the pure component isotherms as well as the mixture isotherms. This allowed us to make a detailed comparison of the different methods to predict the mixture isotherms. All approaches rely on an accurate description of the pure component isotherms and a model to predict the mixture isotherms. As we are interested in low CO₂ concentrations, it is essential that these models correctly predict the low pressure limit, i.e., give a correct description of the Henry regime. Among the equations that describe this limit correctly, the dual-site Langmuir (DSL) model is often used for the pure components and the extended DSL ...

Latest version: v1
Publication date: Jun 20, 2022

Excited-state properties for extended systems: efficient hybrid density functional methods


Anna-Sophia Hehn, Beliz Sertcan, Fabian Belleflamme, Sergey K. Chulkov, Matthew B. Watkins, Jürg Hutter

  • Time-dependent density functional theory has become state-of-the-art for describing photophysical and photochemical processes in extended materials due to its affordable cost. The inclusion of exact exchange was shown to be essential for the correct description of the long-range asymptotics of electronic interactions and thus a well-balanced description of valence, Rydberg and charge-transfer excitations. Several approaches for an efficient treatment of exact exchange have been established for the ground state, while implementations for excited-state properties are rare. Furthermore, the high computational costs required for excited-state properties in comparison to ground-state computations often hinder large-scale applications on periodic systems with hybrid functional accuracy. We therefore propose two approximate schemes for improving computational efficiency for the treatment of exact exchange. Within the auxiliary density matrix method (ADMM), exact exchange is estimated ...

Latest version: v1
Publication date: Jun 17, 2022

Enhancement of exchange bias and perpendicular magnetic anisotropy in CoO/Co multilayer thin films by tuning the alumina template nanohole size


Mohamed Salaheldeen, Ayman Nafady, Ahmed M. Abu-Dief, Rosario Díaz Crespo, María Paz Fernández -García, Juan Pedro Andrés, Ricardo López Antón, Jesús A. Blanco, Pablo Álvarez-Alonso

  • The interest in magnetic nanostructures exhibiting perpendicular magnetic anisotropy and ex-change bias effect has increased in recent years owing to their applications in a new generation of spintronic devices that combine several functionalities. We present a nanofabrication process used to induce perpendicular magnetic anisotropy and exchange bias. 30-nm-thick CoO/Co multilayers were deposited on nanostructured alumina templates with a broad range of pore diameters, 34 nm ≤ Dp ≤ 96 nm, while maintaining the hexagonal lattice parameter at 107 nm. Increase of both the exchange bias field (HEB) and the coercivity (HC) (12 times and 27 times, respectively) was ob-served in the nanostructured films compared to the non-patterned film. The marked dependence of HEB and HC with antidot hole diameters pinpoints to an in-plane to out-of-plane changeover of the magnetic anisotropy at a nanohole diameter of ∼ 75 nm. Micromagnetic simulation shows the existence of antiferromagnetic layers ...

Latest version: v1
Publication date: Jun 16, 2022

The mapped gaussian process (MGP) force-field of Cu-Zn surface alloy


Harry Handoko Halim, Yoshitada Morikawa

  • The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning technique called Gaussian Process as implemented in FLARE package (https://github.com/mir-group/flare). Active and on-the-fly learning were employed to build the database efficiently. The simulation reveals atomistic details of the alloying process, i.e., the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation.

Latest version: v1
Publication date: Jun 15, 2022

Force-based method to determine the potential dependence in electrochemical barriers


Sudarshan Vijay, Georg Kastlunger, Joseph Gauthier, Anjli Patel, Karen Chan

  • Determining ab-initio potential dependent energetics are critical to investigating mechanisms for electrochemical reactions. While methodology for evaluating reaction thermodynamics is established, simulation techniques for the corresponding kinetics is still a major challenge owing to a lack of potential control, finite cell size effects or computational expense. In this work, we develop a model which allows for computing electrochemical activation energies from just a handful of Density Functional Theory (DFT) calculations. The sole input into the model are the atom centered forces obtained from DFT calculations performed on a homogeneous grid composed of varying field-strengths. We show that the activation energies as a function of the potential obtained from our model are consistent for different super-cell sizes and proton concentrations for a range of electrochemical reactions. This record contains output files from all the DFT calculations needed to reproduce the figures in the manuscript.

Latest version: v1
Publication date: Jun 15, 2022

HP - A code for the calculation of Hubbard parameters using density-functional perturbation theory


Iurii Timrov, Nicola Marzari, Matteo Cococcioni

  • We introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive supercells of the traditional linear-response approach; instead, unit cells are used with monochromatic perturbations that significantly reduce the computational cost of determining Hubbard parameters. HP is an open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO. As with other components, HP is optimized to run on a variety of different platforms, from laptops to massively parallel architectures, using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing Hubbard parameters self-consistently for the phospho-olivine LixMn0.5Fe0.5PO4 (x=0, 0.5, 1) and by highlighting the accuracy ...

Latest version: v1
Publication date: Jun 13, 2022

Viscosity in water from first-principles and deep-neural-network simulations


Cesare Malosso, Linfeng Zhang, Roberto Car, Stefano Baroni, Davide Tisi

  • We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

Latest version: v2
Publication date: Jun 10, 2022

Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts


Pablo G. Lustemberg, Sanjaya D. Senanayake, Jose A. Rodriguez, M. Verónica Ganduglia-Pirovano

  • The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H, and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon. Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst ...

Latest version: v1
Publication date: Jun 10, 2022

Ab initio simulation of structure and properties in Ni-based superalloys Haynes282 and Inconel740


Wai-Yim Ching, Saro San, Caizhi Zhou, Ridwan Sakidja

  • The electronic structure, interatomic bonding, and mechanical properties of two supercell models of Ni-based super alloys are calculated by using ab initio density functional theory methods. The alloys are in the face centered cubic lattice having 864 atoms and eleven elements: Haynes282 (Ni₄₄₄Cr₂₀₂Co₇₉Al₅₆Ti₂₅Mo₂₅Fe₁₂Mn₃Si₅C₁₂B) and Inconel740 (Ni₃₇₃Cr₂₄₆Co₁₅₃Al₃₃Ti₂₁Nb₆MoFe₆ Mn₃Si₁₇C₅). These multi-component alloys have very complex electronic structure, bonding and partial charge distributions depending on the composition and strength of local bonding environment. We employ the novel concept of total bond order density (TBOD) and its partial components (PBOD) to ascertain the internal cohesion that controls the intricate balance between the propensity of metallic bonding between Ni, Cr and Co, and the strong bonds with C and Al. We find Inconel740 has slightly stronger mechanical properties than Haynes282. Comparison with more conventional high entropy alloys with equal components are discussed.

Latest version: v1
Publication date: Jun 10, 2022

Stability and magnetic behavior of exfoliable nanowire 1D materials


Joshua Paul, Janet Lu, Sohum Shah, Stephen Xie, Richard Hennig

  • Low-dimensional materials can display enhanced electronic, magnetic, and quantum properties. However, 1D exfoliable nanowires have not been explored as much as their 2D and 0D counterparts. To address this, we use the topological scaling algorithm to identify all sufficiently metastable materials in the Materials Project database which have bulk crystals with one-dimensional (1D) structural motifs. We narrow our search to 263 bulk precursors which exfoliate unique 1D nanowires and contain d-orbital valence electrons. After exfoliating nanowires from these bulk precursors and applying structural optimization, we determine thermodynamic stability in both exfoliation energy (per-atom) and line tension (per-Angstrom) units, the latter of which we argue is a better predictor of stability in 1D materials. We further calculate the ferromagnetic ordering of these isolated nanowire materials. This repository reports the final atomic structure, thermodynamic stability, magnetic moment ...

Latest version: v1
Publication date: Jun 10, 2022

Ranking the synthesizability of hypothetical zeolites with the sorting hat


Benjamin A. Helfrecht, Giovanni Pireddu, Rocio Semino, Scott M. Auerbach, Michele Ceriotti

  • Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “zeolite sorting hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the zeolite sorting hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that ...

Latest version: v1
Publication date: Jun 10, 2022

Numerical simulation of an electrochemical system and semi-analytical method


Farid Taherkhani, Doriano Brogioli, Fabio La Mantia

  • Electrochemical systems are often simulated by using numerical methods based on finite element solution of differential equations. We developed a method to decrease the needed computational resources, based on the analytical solution of a part of the system: the obtained analytical equations are applied as boundary conditions to the finite element calculation. The part of the system that is analytically solved is the region of the diffuse double layer. We provide two COMSOL models: i) a 1d fully numerical calculation and ii) the same simulation performed with the semi-analytical method. We also provide examples of the resulting impedances and frequency responses of various parameters.

Latest version: v1
Publication date: Jun 03, 2022

Shadow-light images of simulated 25 classes of surface roughness for automatic classification


Janusz V. Kozubal, Ahmad Hassanat, Ahmad S. Tarawneh, Roman J. Wróblewski, Hubert Anysz, Jónatas Valença, Eduardo Júlio

  • Many relationships important to civil engineering depend on surface roughness (morphology). Examples are the bond strength between concrete layers, the adhesion of a wheel to the pavement, the angle of friction in the soil in contact with a wall surface, and many other cases when we deal with a material with a surface having the characteristics of a Gaussian field. Based on scans of the natural concrete surfaces subjected to different smoothing processes, theoretical models were made. The observed features of the models were grouped into 25 categories belonging to the spherical semivariogram model. Each category is described by two parameters: range (with discrete domain 0.01, 0.04, 0.08, 0.16, 0.32) and upper limits (also with discrete domain 1, 2, 4 , 8, 16) with zero trend. For all combinations of range-limit pairs, homogeneous Gaussian random fields satisfying the spatial dependence of the category were generated in R software by using the RandomFields library. In the final ...

Latest version: v1
Publication date: Jun 03, 2022

Dynamic response of oxygen vacancies on the Deacon reaction over reduced single crystalline CeO₂-x(111) surfaces


V. Koller, C. Sack, P. Lustemberg, M. V. Ganduglia-Pirovano, H. Over

  • The heterogeneously catalyzed HCl oxidation reaction (Deacon reaction) over ceria leads under typical reaction conditions to a reduction and surface chlorination of CeO2. The reduced single crystalline CeO2-x(111) model surface stabilizes various ordered surface structures, e.g. (√7 × √7)R19.1°, (3 × 3), or (4 × 4), depending on the concentration of oxygen vacancies (VO). Saturating these phases with HCl at room temperature, followed by annealing to the process temperature of 700 K, leads in all cases to a uniformly covering (√3 × √3)R30° overlayer structure with identical Cl coverage and identical adsorption geometry. Low energy electron diffraction (LEED) fingerprinting, density functional theory (DFT) calculations and X-ray photoelectron spectroscopy (XPS) evidence that Cl adsorbs into the O-vacancy at the surface (Clvac) with a high adsorption energy (>2 eV). From thermal desorption spectroscopy (TDS) and XPS of Cl 2p the adsorption energy of Clvac and the water formation is ...

Latest version: v1
Publication date: May 24, 2022

Efficient, interpretable graph neural network representation for angle-dependent properties and its application to optical spectroscopy


Tim Hsu, Tuan Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, S. Roger Qiu, Xiao Chen, Brandon Wood

  • Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions representing transitions between more common geometries exhibiting the strongest ...

Latest version: v1
Publication date: May 23, 2022

Locating guest molecules inside metal-organic framework pores with a multiscale computational approach


Michelle Ernst, Tomasz Poręba, Lars Gnägi, Ganna Gryn'ova

  • Molecular docking has traditionally mostly been employed in the field of protein-ligand binding. In the publication associated with this data, we extend this method, in combination with DFT-level geometry optimizations, to locate guest molecules inside the pores of metal-organic frameworks. Additional information on the adsorption strength in the studied host-guest systems emerges from the computed interaction energies. This record contains inputs and outputs of the molecular docking and the DFT computations.

Latest version: v1
Publication date: May 10, 2022

A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning


Xiangyu Chen, William Shao, Nam Le, Paulette Clancy

  • Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically large computational burden of using ab initio molecular dynamics on the other. In this paper, we use an “on-the-fly” active learning technique to develop a non-parameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field suitable to capture the crystallization of gallium nitride (GaN) using a novel additive manufacturing route and a combination of liquid Ga and ammonia gas precursors to grow GaN thin films. We show that this machine learning model is capable of producing a transferable force field that can model all three phases, solid, liquid and gas, involved in this additive manufacturing process. We verified our computational results against a range of experimental measurements and ...

Latest version: v1
Publication date: May 09, 2022

Donor-acceptor-donor “hot exciton” triads for high reverse intersystem crossing in OLEDs


Yanan Zhu, Sergi Vela, Hong Meng, Clémence Corminboeuf, Maria Fumanal

  • Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high Reversed InterSystem Crossing (hRISC) between a high-lying triplet (Tn, n≥2) and a radiative singlet (Sm). In recent years, donor–acceptor-donor (D-A-D) molecular systems have shown great promise in its ability to enhance the hRISC process under certain conditions. However, strategies to find appropriate D-A-D combinations beyond trial-and-error are still elusive. This work exposes the limited applicability of the current fragment-based design rules and proposes high-throughput screening as the optimal route to find promising candidates that fulfill the energy criteria for hot exciton materials. The strategy consists of first establishing the thresholds for large triplet-triplet splitting and small singlet-triplet gap, then filtering combinations through rate comparison of competitive crossing pathways, and finally confirming hRISC with spin-orbital ...

Latest version: v1
Publication date: May 06, 2022

Impact of glutamate carboxylation in the adsorption of the alpha-1 domain of osteocalcin to hydroxyapatite and titania


Sarah Alamdari, Jim Pfaendtner

  • One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the goal of capturing the fundamental mechanistic and thermodynamic forces that govern changes in these NCP recognition domains as a function of γ-carboxyglutamic acid (Gla) post-translational modification and surface chemistry, we probe the adsorption process of the most commonly occurring NCP, osteocalcin, onto a mineral and metal oxide surface. Here, we apply two enhanced sampling methods to independently probe the effects of post-translational modification and peptide structure on adsorption. First, well-tempered metadynamics was used to capture the binding of acetyl and N-methylamide capped glutamic acid and Gla single amino acids onto crystalline hydroxyapatite and titania model surfaces at physiological pH. Following this, parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) was ...

Latest version: v2
Publication date: Apr 27, 2022

cell2mol: encoding chemistry to interpret crystallographic data


Sergi Vela, Ruben Laplaza, Yuri Cho, Clemence Corminboeuf

  • The creation and maintenance of crystallographic data repositories is one of the greatest data-related achievements in chemistry. Platforms such as the Cambridge Structural Database host what is likely the most diverse collection of synthesizable molecules. If properly mined, they could be the basis for the large-scale exploration of new regions of the chemical space using quantum chemistry (QC). However, it is currently challenging to retrieve all the necessary information for QC based exclusively on the available structural data, especially for transition metal complexes. To solve this shortcoming, we present cell2mol, a software that interprets crystallographic data and retrieves the connectivity and total charge of molecules, including the oxidation state (OS) of metal atoms. We prove that cell2mol outperforms other popular methods at assigning the metal OS, while offering a much more comprehensive interpretation of the unit cell, and we make publicly available reliable ...

Latest version: v1
Publication date: Apr 25, 2022

Enhanced photodegradation of dimethoxybenzene isomers in/on ice compared to in aqueous solution


Ted Hullar, Theo Tran, Zekun Chen, Fernanda C. Bononi, Oliver Palmer, Davide Donadio, Cort Anastasio

  • Photochemical reactions of contaminants in snow and ice can be important sources and sinks for various organic and inorganic compounds. Snow contaminants can be found in the bulk ice matrix, in internal liquid-like regions (LLRs), or in quasi-liquid layers (QLLs) at the air-ice interface, where they can readily exchange with the firn air. Some studies have reported that direct photochemical reactions occur faster in LLRs and QLLs than in aqueous solution, while others have found similar rates. Here, we measure the photodegradation rate constants of the three dimethoxybenzene isomers under varying experimental conditions, including in aqueous solution, in LLRs, and at the air-ice interface of nature-identical snow. Relative to aqueous solution, we find modest photodegradation enhancements (3- and 6-fold) in LLRs for two of the isomers, and larger enhancements (15- to 30-fold) at the air-ice interface for all three isomers. We use computational modeling to assess the impact of light ...

Latest version: v1
Publication date: Apr 20, 2022

A data-science approach to predict the heat capacity of nanoporous materials


Seyed Mohamad Moosavi, Balázs Álmos Novotny, Daniele Ongari, Elias Moubarak, Mehrdad Asgari, Özge Kadioglu, Charithea Charalambous, Andres Ortega-Guerrero, Amir H. Farmahini, Lev Sarkisov, Susana Garcia, Frank Noé, Berend Smit

  • The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.

Latest version: v1
Publication date: Apr 13, 2022

BELLO: A post-processing tool for the local-order analysis of disordered systems


Behnood Dianat, Francesco Tavanti, Andrea Padovani, Luca Larcher, Arrigo Calzolari

  • The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical investigations. The properties of short- and medium-range local order structures are responsible for the electronic, optical and transport properties of these systems. Here, we present the BELLO open source code, a post-processing script-tool created for the automatic analysis and extraction of structural characteristics of disordered and amorphous systems. BELLO is agnostic to the code that generated single configurations or trajectories, it provides an intuitive access through a graphical user interface (GUI), and it requires minimal computational resources. Its capabilities include the calculation of the order parameter , the folded structure identification, and statistical analysis tools such as atomic coordination number and pair/angle-distribution functions. The working principles of the code are described ...

Latest version: v1
Publication date: Apr 13, 2022

Is there a polaron signature in angle-resolved photoemission of CsPbBr₃?


Maryam Sajedi, Maxim Krivenkov, Dmitry Marchenko, Jaime Sánchez-Barriga, Anoop K. Chandran, Andrei Varykhalov, Emile D. L. Rienks, Irene Aguilera, Stefan Blügel, Oliver Rader

  • The formation of large polarons has been proposed as reason for the high defect tolerance, low mobility, low charge carrier trapping and low nonradiative recombination rates of lead halide perovskites. Recently, direct evidence for large-polaron formation has been reported from a 50% effective mass enhancement in angle-resolved photoemission of CsPbBr₃ over theory for the orthorhombic structure. We present in-depth band dispersion measurements of CsPbBr₃ and GW calculations which lead to almost identical effective masses at the valence band maximum of 0.203+/-0.016 m₀ in experiment and 0.217 m₀ in orthorhombic theory. We argue that the effective mass can be explained solely on the basis of electron-electron correlation and large polaron formation cannot be concluded from photoemission data.

Latest version: v1
Publication date: Apr 11, 2022

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis


Pushkar Ghanekar, Siddharth Deshpande, Jeffrey Greeley

  • Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to ...

Latest version: v1
Publication date: Apr 11, 2022

Superconductivity in antiperovskites


Noah Hoffmann, Tiago F. T. Cerqueira, Jonathan Schmidt, Miguel A. L. Marques

  • We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ₃ where X and Z are metals and Y is H, B, C, N, O, and P. Our starting point are electron-phonon calculations for 384 materials performed with density-functional perturbation theory. While 40% of the materials were dynamically unstable as they exhibited imaginary frequencies, we discovered 16 compounds with Tc higher than 5 K including antiperovskites with Y=H, N, C and O. We used these results to train interpretable machine learning models to understand and further explore this family of compounds. This lead us to predict a further 44 materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe₃. Furthermore, the models give us an understanding of the mechanism of superconductivity in anti-perovskites and highlight the importance of the density of states at the Fermi level and of the mass of the Y-atom ...

Latest version: v1
Publication date: Apr 06, 2022

Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches


Eleftherios Andritsos, Kevin Rossi

  • Li–S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move ...

Latest version: v2
Publication date: Apr 05, 2022

High-mobility semiconducting polymers with different spin ground states


Xiao-Xiang Chen, Jia-Tong Li, Yu-Hui Fang, Xin-Yu Deng, Xue-Qing Wang, Guangchao Liu, Yunfei Wang, Xiaodan Gu, Shang-Da Jiang, Ting Lei

  • Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (ΔES-T) could enable small ΔES-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state (S = 1) could exhibit doublet (S = 1/2) behavior due to different spin distributions ...

Latest version: v1
Publication date: Apr 01, 2022

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials


Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky

  • This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables ...

Latest version: v1
Publication date: Mar 30, 2022

Unified theory of atom-centered representations and message-passing machine-learning schemes


Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, Michele Ceriotti

  • Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent ...

Latest version: v1
Publication date: Mar 24, 2022

Optimizing the thermodynamics and kinetics of the triplet-pair dissociation in donor-acceptor copolymers for intramolecular singlet fission


Maria Fumanal, Clemence Corminboeuf

  • Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials in particular, have attracted growing interest as they can be easily implemented in single junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, which mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. ...

Latest version: v1
Publication date: Mar 23, 2022

Assessing the persistence of chalcogen bonds in solution with neural network potentials


Veronika Jurásková, Frédéric Célerse, Rubén Laplaza, Clémence Corminboeuf

  • Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of ...

Latest version: v1
Publication date: Mar 16, 2022

Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents


Olivia P. Pfeiffer, Haihao Liu, Luca Montanelli, Marat I. Latypov, Fatih G. Sen, Vishwanath Hegadekatte, Elsa A. Olivetti, Eric R. Homer

  • Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Latest version: v3
Publication date: Mar 16, 2022

One-shot approach for enforcing piecewise linearity on hybrid functionals: application to band-gap predictions


Jing Yang, Stefano Falletta, Alfredo Pasquarello

  • We present an efficient procedure for constructing nonempirical hybrid functionals to accurately predict band gaps of extended systems. We determine mixing parameters by enforcing the generalized Koopmans’ condition on localized electron states, which are achieved by inserting an optimized potential probe. Application of this scheme to a large set of materials yields band gaps with a mean error of 0.30 eV with respect to experiment. Next, we consider a perturbative one-shot approach in which the single- particle eigenvalues are calculated with the wave functions obtained at the semilocal level. In this way, the computational cost is reduced by ∼85% without loss of accuracy. The scheme is found to be robust upon consideration of different defect species and functional forms.

Latest version: v1
Publication date: Mar 15, 2022

Machine learning for metallurgy: neural network potentials for Al-Cu-Mg and Al-Cu-Mg-Zn


Daniel Marchand, W.A. Curtin

  • Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest cannot be measured experimentally. Accurate simulation methods are essential but first-principles density functional theory (DFT) is prohibitively expensive while empirical interatomic potentials are rarely sufficiently accurate for alloys. Machine learning (ML) is emerging as an approach to create computationally-efficient atomistic potentials achieving near-DFT accuracy. Building on recent work on binary Al-Cu and ternary Al-Mg-Si, here a family of neural network potentials (NNPs) for Al alloys of Al-Cu-Mg and Al-Cu-Mg-Zn is developed and assessed using the Behler-Parinello formulation. Training of the potentials uses a robust set of metallurgically-relevant structures including intermetallic phases, stacking faults, solute/solute and solute/stacking fault interactions, solute clusters, and ...

Latest version: v2
Publication date: Mar 14, 2022

Materials Cloud three-dimensional crystals database (MC3D)


Sebastiaan Huber, Marnik Bercx, Nicolas Hörmann, Martin Uhrin, Giovanni Pizzi, Nicola Marzari

  • The Materials Cloud three-dimensional database is a curated set of relaxed three-dimensional crystal structures based on raw CIF data taken from the external experimental databases MPDS, COD and ICSD. The raw CIF data have been imported, cleaned and parsed into a crystal structure; their ground-state has been computed using the SIRIUS-enabled pw.x code of the Quantum ESPRESSO distribution, and tight tolerance criteria for the calculations using the SSSP protocols. This entire procedure is encoded into an AiiDA workflow which automates the process while keeping full data provenance. Here, since the original source data of the ICSD and MPDS databases are copyrighted, only the provenance of the final SCF calculation on the relaxed structures can be made publicly available. The MC3D ID numbers come from a list of unique "parent" stoichiometric structures that has been created and curated from a collection of these experimental databases. Once a parent structure has been optimized ...

Latest version: v1
Publication date: Mar 12, 2022

On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials


Henglu Xu, Raffaela Cabriolu, Berend Smit

  • If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature. For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations ...

Latest version: v2
Publication date: Mar 09, 2022

Entanglement between a muon spin and I>1/2 nuclear spins


Pietro Bonfà, Jonathan Frassineti, John M. Wilkinson, Giacomo Prando, Muhammad M. Isah, Chennan Wang, Tiziana Spina, Boby Joseph, Vesna F. Mitrović, Roberto De Renzi, Stephen J. Blundell, Samuele Sanna

  • We report on the first example of quantum coherence between the spins of muons and quadrupolar nuclei. We observe this effect in vanadium intermetallic compounds which adopt the A15 crystal structure, and whose members include all technologically dominant superconductors. The entangled states are extremely sensitive to the local structural and electronic environments through the electric field gradient at the quadrupolar nuclei. This case-study demonstrates that positive muons can be used as a quantum sensing tool to probe also structural and charge related phenomena in materials, even in the absence of magnetic order. The data here contained can be used to reproduce all results and graphs shown in the article.

Latest version: v1
Publication date: Mar 08, 2022

Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature: corresponding atomic structures at the certain temperature


Haiyuan Wang, Alexey Tal, Thomas Bischoff, Patrick Gono, Alfredo Pasquarello

  • We develop a computationally efficient scheme to accurately determine finite-temperature band gaps. We here focus on materials belonging to the class ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide perovskites. First, an initial estimate of the band gap is provided for the ideal crystalline structure through the use of a range-separated hybrid functional, in which the parameters are determined nonempirically from the electron density and the high-frequency dielectric constant. Next, we consider two kinds of band-gap corrections to account for spin-orbit coupling and thermal vibrations including zero-point motions. In particular, the latter effect is accounted for through the special displacement method, which consists in using a single distorted configuration obtained from the vibrational frequencies and eigenmodes, thereby avoiding lengthy molecular dynamics. The sequential consideration of both corrections systematically improves the band gaps, ...

Latest version: v1
Publication date: Mar 04, 2022

On the robust extrapolation of high-dimensional machine learning potentials


Claudio Zeni, Andrea Anelli, Aldo Glielmo, Kevin Rossi

  • We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside the convex hull defined by the training set points. We then propose a perspective to rationalise the domain of robust extrapolation and accurate prediction of atomistic machine learning potentials in terms of the probability density induced by training points in the representation space. The data here contained can be used to reproduce all results and graphs shown in the article. We also include the trajectory files for the Au13 dataset we generate by running molecular dynamics simulations of an Au nanoparticle containing 13 atoms at temperatures of 50K, 100K, 200K, 300K, and 400K. Details regarding the generation of such dataset can be found in the supplementary information file for the article.

Latest version: v1
Publication date: Mar 03, 2022

How robust is the reversible steric shielding strategy for photoswitchable organocatalysts?


Simone Gallarati, Raimon Fabregat, Veronika Juraskova, Theo Jaffrelot Inizan, Clemence Corminboeuf

  • A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and non-invasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts’ conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realisation of reversible steric shielding for the photocontrol ...

Latest version: v1
Publication date: Feb 24, 2022

A microscopic picture of paraelectric perovskites from structural prototypes


Michele Kotiuga, Samed Halilov, Boris Kozinsky, Marco Fornari, Nicola Marzari, Giovanni Pizzi

  • This work details how to determine structural prototypes for the cubic perovskite structure that are used to study the B-site displacements in the cubic, paraelectric phase. Car-Parrinello MD simulations of cubic barium titanate (BaTiO3) show the titanium displacements from the undistorted cubic structure. Using a systematic symmetry analysis we construct microscopic templates, i.e. representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. Density functional theory calculations, using the microscopic templates as starting structures for a relaxation, are carried out to find structural prototypes of BaTiO3 with local polar distortions but with cubic point symmetry. The stability of these structures is studied as a function of volume and with respect to the zone-boundary phonons of pristine cubic BaTiO3. The stable distortions patterns for BaTiO3 are investigated for other titanates and for a handful of niobates and zirconates.

Latest version: v2
Publication date: Feb 22, 2022

Low-temperature crystallography and vibrational properties of rozenite (FeSO₄·4H₂O), a candidate mineral component of the polyhydrated sulfate deposits on Mars


Johannes M. Meusburger, Karen A. Hudson-Edwards, Chiu C. Tang, Eamonn T. Connolly, Rich A. Crane, A. Dominic Fortes

  • Rozenite (FeSO₄·4H₂O) is a candidate mineral component of the polyhydrated sulfate deposits on the surface and in the subsurface of Mars. In order to better understand its behavior at temperature conditions prevailing on the martian surface and aid its identification in ongoing and future Rover missions we have carried out a combined experimental and computational study of the mineral’s structure and properties. We collected neutron powder diffraction data at temperatures ranging from 21 – 290 K, room temperature synchrotron X-ray data and Raman spectra. Moreover, first-principles calculations of the vibrational properties of rozenite were carried out to aid the interpretation of the Raman spectrum. In this work, we demonstrated how combining Raman spectroscopy and X-ray diffraction of the same sample material sealed inside a capillary with complementary first principles calculations yields accurate reference Raman spectra. This workflow enables the construction of a reliable ...

Latest version: v1
Publication date: Feb 18, 2022

Interaction of water with nitrogen-doped graphene


Azim Fitri Ainul Abidin, Ikutaro Hamada

  • We have studied the interaction of water and graphene doped with nitrogen in different configurations, namely, graphitic and pyridinic nitrogen, by means of the van der Waals density functional. We found that the local nitrogen configuration plays a key role in determining the stable water configuration, while the dispersion force is responsible for the water adsorption. With the graphitic nitrogen, water prefers to orient with its oxygen toward the surface, whereas for the pyridinic nitrogen it prefers to orient with its hydrogens toward the surface, because nitrogen is positively and negatively charged for the former and the latter, respectively. Our results have great implications for the modeling of the interface between water and nitrogen-doped graphitic systems.

Latest version: v1
Publication date: Feb 18, 2022

Landau levels as a probe for band topology in graphene moiré superlattices


QuanSheng Wu, Jianpeng Liu, Yifei Guan, Oleg V. Yazyev

  • We propose Landau levels as a probe for the topological character of electronic bands in two-dimensional moiré superlattices. We consider two configurations of twisted double bilayer graphene (TDBG) that have very similar band structures, but show different valley Chern numbers of the flat bands. These differences between the AB-AB and AB-BA configurations of TDBG clearly manifest as different Landau level sequences in the Hofstadter butterfly spectra calculated using the tight-binding model. The Landau level sequences are explained from the point of view of the distribution of orbital magnetization in momentum space that is governed by the rotational C2 and time-reversal T symmetries. Our results can be readily extended to other twisted graphene multilayers and h-BN/graphene heterostructures thus establishing the Hofstadter butterfly spectra as a powerful tool for detecting the nontrivial valley band topology.

Latest version: v1
Publication date: Feb 17, 2022

The JuHemd (Jülich-Heusler-magnetic-database) of the Monte Carlo simulated critical temperatures of the magnetic phase transition for experimentally reported Heusler and Heusler-like materials


Roman Kováčik, Phivos Mavropoulos, Stefan Blügel

  • The JuHemd (Jülich-Heusler-magnetic-database) is a collection of the magnetic phase transition types and transition temperatures (Tc) for experimentally documented Heusler and Heusler-like materials, as found by density functional calculations augmented by the Monte Carlo method, and as reported by experiment in the literature. The database contains results on 400 compounds, many of them with different setups of the chemical order/disorder, totaling 776 systems. The ground state electronic structure was obtained by density functional theory calculations with the JuKKR code. Two exchange-correlation functionals were employed: the local density approximation (LDA) and the generalized gradient approximation (GGA). The chemical disorder was treated within the coherent-potential approximation. For 306 materials (627 systems) with sizable magnetization, the Heisenberg exchange parameters were evaluated using the method of infinitesimal rotations and the Tc was determined by our in-house ...

Latest version: v1
Publication date: Feb 17, 2022

Quantum phase diagram of high-pressure hydrogen


Lorenzo Monacelli, Michele Casula, Kosuke Nakano, Sandro Sorella, Francesco Mauri

  • The interplay between electron correlation and nuclear quantum effects makes our understanding of elemental hydrogen a formidable challenge. Here, we present the phase diagram of hydrogen and deuterium at low temperatures and high-pressure (P > 300 GPa) by accounting for highly accurate electronic and nuclear enthalpies. We evaluated internal electronic energies by diffusion quantum Monte Carlo, while nuclear quantum motion and anharmonicity have been included by the stochastic self-consistent harmonic approximation. Our results show that the long-sought atomic metallic hydrogen, predicted to host room-temperature superconductivity, forms at 577±10 GPa (640±14 GPa in deuterium). Indeed, anharmonicity pushes the stability of this phase towards pressures much larger than previous theoretical estimates or attained experimental values. Before atomization, molecular hydrogen transforms from a metallic phase III to another metallic structure that is still molecular (phase VI) at 422±40 ...

Latest version: v1
Publication date: Feb 16, 2022

Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe₃


Junzhang Ma, Simin Nie, Xin Gui, Muntaser Naamneh, Jasmin Jandke, Chuanying Xi, Jinglei Zhang, Tian Shang, Yimin Xiong, Itzik Kapon, Neeraj Kumar, Yeong Soh, Daniel Gosálbez-Martínez, Oleg V. Yazyev, Wenhui Fan, Hannes Hübener, Umberto De Giovannini, Nicholas Clark Plumb, Milan Radovic, Michael Andreas Sentef, Weiwei Xie, Zhijun Wang, Christopher Mudry, Markus Müller, Ming Shi

  • Charge neutrality and their expected itinerant nature makes excitons potential transmitters of information. However, exciton mobility remains inaccessible to traditional optical experiments that only create and detect excitons with negligible momentum. Here, using angle-resolved photoemission spectroscopy, we detect dispersing excitons in quasi-one-dimensional metallic trichalcogenide, TaSe₃. The low density of conduction electrons and low dimensionality in TaSe₃ combined with a polaronic renormalization of the conduction band and the poorly screened interaction between these polarons and photo-induced valence holes leads to various excitonic bound states that we interpret as intrachain and interchain excitons, and possibly trions. The thresholds for the formation of a photo-hole together with an exciton appear as side valence bands with dispersions nearly parallel to the main valence band, but shifted to lower excitation energies. The energy separation between side and main ...

Latest version: v1
Publication date: Feb 11, 2022

Total energies of atoms from integral-equation radial solver


Jānis Užulis, Andris Gulans

  • We present a numerical tool for solving the non-relativistic Kohn-Sham problem for spherically-symmetric atoms. It treats the Schrödinger equation as an integral equation relying heavily on convolutions. The solver supports different types of exchange-correlation functionals including screened and long-range corrected hybrids. We implement a new method for treating range separation based on the complementary error function kernel. The present tool is applied in spin-restricted non-relativistic total energy calculations of atoms. A comparison with ultra-precise reference data[Cinal, JOMC 58, 1571 (2020)] shows a 14-digit agreement for Hartree-Fock results. We provide further benchmark data obtained with 5 different exchange-correlation functionals: VWN5 (the local-density approximation), PBE (the generalized gradient approximation), PBE0 and B3LYP (hybrids with a Fock exchange) and LC-BLYP (hybrid with a long-range corrected exchange).

Latest version: v1
Publication date: Feb 11, 2022

Photochemical anisotropy and direction-dependent optical absorption properties in semiconductors


Chiara Ricca, Ulrich Aschauer

  • Photochemical reactions on semiconductors are anisotropic, since they occur with different rates on surfaces of different orientation. Understanding the origin of this anisotropy is crucial to engineering more efficient photocatalysts. In this work, we use hybrid density functional theory (DFT) to identify the surfaces associated with the largest number of photo-generated carriers in different semiconductors. For each material we create a spherical heat map of the probability of optical transitions at different wave vectors. These maps allow to identify the directions associated with the majority of the photo-generated carriers and can thus be used to make predictions about the most reactive surfaces for photochemical applications. Results indicate that it is generally possible to correlate the heat maps with the anisotropy of the bands observed in conventional band-structure plots, as previously suggested. However, we also demonstrate that conventional bands-structure plots do ...

Latest version: v1
Publication date: Feb 07, 2022

Investigating finite-size effects in computer simulations of superionic materials


Federico Grasselli

  • The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the alpha phase of AgI (type I). Largely validated empirical force-fields are employed to run ns-long simulations and extract general trends for several properties, at increasing size and in a wide temperature range. This work shows that, for the considered type-II superionic conductors, the diffusivity dramatically depend on the system size and that the superionic regime is shifted to larger temperatures in smaller cells. Furthermore, only simulations of several hundred atoms are able to capture the experimentally-observed, characteristic change in the activation energy of the diffusion process, occurring at the order-disorder transition to the superionic regime. Finite-size effects on ion diffusion are instead much weaker in ...

Latest version: v1
Publication date: Feb 04, 2022

Training sets based on uncertainty estimates in the cluster-expansion method


David Kleiven, Jaakko Akola, Andrew Peterson, Tejs Vegge, Jin Hyun Chang

  • Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a "bootstrapping uncertainty structure selection" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each `.db` file contains 100 DFT calculations, which were ...

Latest version: v1
Publication date: Feb 03, 2022

Controlling the TiN electrode work function at the atomistic level: a first principles investigation


Arrigo Calzolari, Alessandra Catellani

  • The paper reports on a theoretical description of work function of TiN, which is one of the most used materials for the realization of electrodes and gates in CMOS devices. Indeed, although the work function is a fundamental quantity in quantum mechanics and also in device physics, as it allows the understanding of band alignment at heterostructures and gap states formation at the metal/semiconductor interface, the role of defects and contaminants is rarely taken into account. Here, by using first principles simulations, we present an extensive study of the work function dependence on nitrogen vacancies and surface oxidation for different TiN surface orientations. The results complement and explain a number of existent experimental data, and provide a useful tool to tailoring transport properties of TiN electrodes in device simulations.

Latest version: v1
Publication date: Feb 01, 2022

Phase-field investigation of lithium electrodeposition under different applied overpotentials and operating temperatures


Joonyeob Jeon, Gil Ho Yoon, Tejs Vegge, Jin Hyun Chang

  • Despite the high promise of Li-metal-based batteries, its commercialization has been hampered due to the formation of dendrites that lead to mechanical instability, energy loss and eventual internal short circuits. The provided dataset consists of the phase-field simulation results for investigating the effect of applied overpotential and operating temperature on dendrite growth. These data are used for elucidating the correlation of overpotential and temperature with the surface modulation during electrodeposition. The simulation cell consists of a Li metal anode and 1M LiPF6 in EC:DMC (1:1), and the electrodeposition process was simulated under the applied overpotential ranging from 0.30 V to 0.44 V with a 0.02 V increment at temperatures from 268 K and 333 K with a 5 K increment. The data contains order parameter, chemical potential and overpotential. The supplied Python script can compute the surface tortuosity, average and maximum Li heights and dendrite height at a given temperature, overpotential and time step.

Latest version: v1
Publication date: Feb 01, 2022

The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations


Nakib Protik, Chunhua Li, Miguel Prudena, David Broido, Pablo Ordejon

  • elphbolt is a modern Fortran (2018 standard) code for efficiently solving the coupled electron-phonon Boltzmann transport equations from first principles. Using results from density functional and density functional perturbation theory as inputs, it can calculate the effect of the non-equilibrium phonons on the electronic transport (phonon drag) and non-equilibrium electrons on the phononic transport (electron drag) in a fully self-consistent manner and obeying the constraints mandated by thermodynamics. It can calculate the lattice, charge, and thermoelectric transport coefficients for the temperature gradient and electric fields, and the effect of the mutual electron-phonon drag on these transport properties. The code fully exploits the symmetries of the crystal and the transport-active window to allow the sampling of extremely fine electron and phonon wave vector meshes required for accurately capturing the drag phenomena. The coarray feature of modern Fortran, which offers ...

Latest version: v1
Publication date: Feb 01, 2022

Structure database of glass-ceramic lithium thiophosphate electrolytes


Haoyue Guo, Nongnuch Artrith

  • This database contains computationally generated atomic structures of glass-ceramics lithium thiophosphates (gc-LPS) with the general composition (Li2S)x(P2S5)1-x in the XCrySDen structure format (XSF). Total energies and interatomic forces from density-functional theory (DFT) calculations are included as additional meta information. The extended XSF format is compatible with the atomic energy network (ænet) package for artificial neural network (ANN) potential construction and application. The DFT calculations used projector-augmented-wave (PAW) pseudopotentials and the Perdew−Burke−Ernzerhof (PBE) exchange-correlation functional as implemented in the Vienna Ab Initio Simulation Package (VASP) and a kinetic energy cutoff of 520 eV. The first Brillouin zone was sampled using VASP’s fully automatic k-point scheme with a length parameter Rk = 25Å. The gc-LPS structures were generated using a combination of different sampling methods. Initial amorphous structure models were generated ...

Latest version: v1
Publication date: Feb 01, 2022

Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network


Sebastian Feldmann, Michael Schmiedt, Julian Schlosser, Wolfgang Rimkus, Tobias Stempfle

  • In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The ...

Latest version: v1
Publication date: Jan 31, 2022

A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials


Tommaso Chiarotti, Nicola Marzari, Andrea Ferretti

  • Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators. The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator. Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such ...

Latest version: v2
Publication date: Jan 28, 2022

On-surface polyarylene synthesis by cycloaromatization of isopropyl substituents


Amogh Kinikar, Marco Di Giovannantonio, José I. Urgel, Kristjan Eimre, Zijie Qiu, Yanwei Gu, Enquan Jin, Akimitsu Narita, Xiao-Ye Wang, Klaus Müllen, Pascal Ruffieux, Carlo Antonio Pignedoli, Roman Fasel

  • In this record we provide the data to support our recent finding on surface catalyzed cycloaromatization. Immobilization of organic building blocks on metal surfaces and their coupling via thermally induced C-C bond formations are developing as an important addition to the toolbox of organic and polymer synthesis. Additional advantages of this technique are the in situ monitoring of the reaction by scanning probe methods and the accessibility of insoluble and reactive carbon nanostructures. The diversity of conceivable products, however, sensitively depends on the number of available on-surface reactions. In the manuscript where the results are discussed, we introduce an unprecedented example, the intermolecular oxidative coupling of isopropyl substituents of arenes. With a new phenylene ring being formed, this [3+3] dimerization can be regarded as a formal cycloaromatization. The synthetic value of this novel reaction is proven by the synthesis of polyarylenes and ...

Latest version: v1
Publication date: Jan 25, 2022

Fatigue database of high entropy alloys


Shiyi Chen, Xuesong Fan, Weidong Li, Baldur Steingrimsson, Peter Liaw

  • Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be able to practically useful if it is prone to fatigue failure. To take the advantage of lately emerged high entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported till the yearend of 2021. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contains 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy composition, key fatigue properties, and additional information influential to or interrelated with fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve.

Latest version: v1
Publication date: Jan 24, 2022

Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys


Yi Hu, William Curtin

  • Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate shearing and Orowan looping are usually considered to have equal strengths. Pseudo-random precipitate microstructures are constructed based on experimental precipitate dimensions and volume fractions at peak aging. A Discrete Dislocation Dynamics method is then adapted to compute the Critical Resolved Shear Stress (CRSS) for Orowan looping of dislocations moving through the non-shearable precipitate field. The CRSS for Orowan looping is determined by a typical in-situ precipitate spacing that is smaller than the average spacing and by the dislocation core energy within a radius of ≈5b, a factor rarely considered. The matrix misfit stresses, volume fraction, and precipitate shape have small effects on the CRSS. With microstructure and property details introduced as faithfully as possible, the CRSS for Orowan ...

Latest version: v1
Publication date: Jan 21, 2022

Genetic optimization of homogeneous catalysts


Ruben Laplaza, Simone Gallarati, Clemence Corminboeuf

  • We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are demonstrated with two examples: i) Ligand optimization and exploration for Ni-catalyzed aryl-ether cleavage manipulating SMILES and using a fitness function derived from molecular volcano plots, ii) multiobjective (i.e., activity/selectivity) optimization of bipyridine N.N'-dioxide Lewis basic organocatalysts for the asymmetric propargylation of benzaldehyde from 3D molecular fragments. We show that evolutionary optimization, enabled by NaviCatGA, is an efficient way of accelerating catalyst discovery that bypasses combinatorial scaling issues and incorporates compelling chemical constraints.

Latest version: v1
Publication date: Jan 21, 2022

Predicting the influence of edge oxidation in parallel-plate rheometry


Varun Venoor, Jo Ann Ratto, David Kazmer, Margaret Sobkowicz

  • Melt post-condensation, thermal, and thermo-oxidative degradation of a cyclo-aliphatic polyamide were studied through time-resolved rheometry (TRR). The implemented TRR elucidates structural changes occurring during two concurrent phenomena, namely melt post-condensation and thermal/thermo-oxidative degradation, during the time sweep in a parallel plate rheometer. TRR measurements were conducted on neat polyamide under nitrogen (inert/non-oxidative) and air (oxidative) environment at 3 % strain amplitude and a range of frequencies between 0.1 and 100 rad/sec for two hours. At temperatures of 260, 270, and 275 °C, a dual-stage time-dependent growth in viscoelastic properties was observed under an oxidative environment. Thermo-oxidative degradation of polyamide melt 270 °C was shown to occur from the exposed sample edge, continuing inwards, effectively reducing the radius of the unoxidized polymer melt by 6.4 %. A modeling approach using MATLAB is presented to interpret and ...

Latest version: v1
Publication date: Jan 18, 2022

Hierarchical short- and medium-range order structures in amorphous Ge_x Se_1–x for selectors applications


Francesco Tavanti, Behnood Dianat, Alessandra Catellani, Arrigo Calzolari

  • In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_x Se_1–x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge–dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, ...

Latest version: v1
Publication date: Jan 18, 2022

Surface and interface effects in oxygen deficient SrMnO3 thin films grown on SrTiO3


Moloud Kaviani, Ulrich Aschauer

  • Complex oxide functionality, such as ferroelectricity, magnetism or superconductivity is often achieved in epitaxial thin-film geometries. Oxygen vacancies tend to be the dominant type of defect in these materials but a fundamental understanding of their stability and electronic structure has so far mostly been established in the bulk or strained bulk, neglecting interfaces and surfaces present in a thin-film geometry. We investigate here, via density functional theory calculations, oxygen vacancies in the model system of a SrMnO3 (SMO) thin film grown on a SrTiO3 (STO) (001) substrate. Structural and electronic differences compared to bulk SMO result mainly from undercoordination at the film surface. The changed crystal field leads to a depletion of subsurface valence-band states and transfer of this charge to surface Mn atoms, both of which strongly affect the defect chemistry in the film. The result is a strong preference of oxygen vacancies in the surface region compared to ...

Latest version: v1
Publication date: Jan 18, 2022

Fully ab-initio electronic structure of Ca₂RuO₄


Francesco Petocchi, Viktor Christiansson, Philipp Werner

  • The reliable ab-initio description of strongly correlated materials is a long-sought capability in condensed matter physics. The GW+EDMFT method is a promising scheme, which provides a self-consistent description of correlations and screening, and does not require user-provided parameters. In order to test the reliability of this approach we apply it to the experimentally well characterized perovskite compound Ca₂RuO₄, in which a temperature-dependent structural deformation drives a paramagnetic metal-insulator transition. Our results demonstrate that the nonlocal polarization and self-energy components introduced by GW are essential for setting the correct balance between interactions and bandwidths, and that the GW+EDMFT scheme produces remarkably accurate predictions of the electronic properties of this strongly correlated material.

Latest version: v1
Publication date: Jan 17, 2022

Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach


Benjamin K. Chang, Jin-Jian Zhou, Nien-En Lee, Marco Bernardi

  • Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two limiting regimes – band transport, characterized by weak e-ph interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in OMCs in the intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of ...

Latest version: v2
Publication date: Jan 12, 2022

Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations


Paolo Pegolo, Stefano Baroni, Federico Grasselli

  • Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li₃ClO antiperovskite. By leveraging ab initio, machine learning, and forcefield descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors. In this record, we provide data and scripts to generate the plots supporting our findings. We also provide the machine learning model and the dataset to train it.

Latest version: v1
Publication date: Jan 11, 2022

On-surface synthesis and characterization of nitrogen-substituted undecacenes


Kristjan Eimre, José I. Urgel, Hironobu Hayashi, Marco Di Giovannantonio, Pascal Ruffieux, Shizuka Sato, Satoru Otomo, Yee Seng Chan, Naoki Aratani, Daniele Passerone, Oliver Gröning, Hiroko Yamada, Roman Fasel, Carlo Antonio Pignedoli

  • In this record, we provide the data supporting our recent results on the synthesis of nitrogen-substituted undecacene analogs. Heteroatom substitution in acenes allows to tailor their remarkable electronic properties, expected to include spin-polarization and magnetism for larger members of the acene family. Here, we present a strategy for the on-surface synthesis of three undecacene analogs substituted with four nitrogen atoms on an Au(111) substrate, by employing specifically designed diethano-bridged precursors. A similarly designed precursor is used to synthesize the pristine undecacene molecule. In the publication where the results are discussed, the experimental features of scanning probe microscopy are compared with ab initio simulations, to demonstrate that the ground state of the synthesized tetraazaundecacene has considerable open-shell character on Au(111). Additionally, we demonstrate that electronegative nitrogen atoms induce a considerable shift in energy level ...

Latest version: v1
Publication date: Jan 11, 2022

Discovery of Ĉ₂ rotation anomaly in topological crystalline insulator SrPb


Wenhui Fan, Simin Nie, Cuixiang Wang, Binbin Fu, Changjiang Yi, Shunye Gao, Zhicheng Rao, Dayu Yan, Junzhang Ma, Ming Shi, Yaobo Huang, Youguo Shi, Zhijun Wang, Tian Qian, Hong Ding

  • Topological crystalline insulators (TCIs) are insulating electronic states with nontrivial topology protected by crystalline symmetries. Recently, theory has proposed new classes of TCIs protected by rotation symmetries Ĉ_n, which have surface rotation anomaly evading the fermion doubling theorem, i.e., n instead of 2n Dirac cones on the surface preserving the rotation symmetry. Here, we report the first realization of the Ĉ_2 rotation anomaly in a binary compound SrPb. Our first-principles calculations reveal two massless Dirac fermions protected by the combination of time-reversal symmetry T̂ and Ĉ_2y on the (010) surface. Using angle-resolved photoemission spectroscopy, we identify two Dirac surface states inside the bulk band gap of SrPb, confirming the Ĉ_2 rotation anomaly in the new classes of TCIs. The findings enrich the classification of topological phases, which pave the way for exploring exotic behavior of the new classes of TCIs.

Latest version: v1
Publication date: Dec 24, 2021

DFT data for giant hardening response in AlMgZn(Cu) alloys


Daniel Marchand, Curtin William

  • AiiDA calculations for the publication Giant hardening response in AlMgZn(Cu) alloys. This study presents a thermomechanical processing concept which is capable of exploiting the full indus- trial application potential of recently introduced AlMgZn(Cu) alloys. The beneficial linkage of alloy design and processing allows not only to satisfy the long-standing trade-off between high mechanical strength in use and good formability during processing but also addresses the need for economically feasible processing times. After an only 3-hour short pre-aging treatment at 100 °C, the two investigated alloys, based on commercial EN AW-5182 and modified with additions of Zn and Zn + Cu respectively, show high formability due to increased work-hardening. Then, these alloys exhibit a giant hardening response of up to 184 MPa to reach a yield strength of 410 MPa after a 20-minute short final heat treatment at 185 °C, i.e. paint-baking. This rapid hardening response strongly depends on the ...

Latest version: v1
Publication date: Dec 21, 2021

Modeling the Ga/As binary system across temperatures and compositions from first principles


Giulio Imbalzano, Michele Ceriotti

  • Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the Ga/As system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably ...

Latest version: v2
Publication date: Dec 20, 2021

Structure-property maps with kernel principal covariates regression


Benjamin A. Helfrecht, Rose K. Cersonsky, Guillaume Fraux, Michele Ceriotti

  • Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression, and can be used to conveniently reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.

Latest version: v2
Publication date: Dec 20, 2021

High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds


Gabriel M. Nascimento, Elton Ogoshi, Adalberto Fazzio, Carlos Mera Acosta, Gustavo M. Dalpian

  • The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 315 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for ...

Latest version: v1
Publication date: Dec 17, 2021

Bound by three-body interactions


Gary Ferkinghoff, Leanna Müller, Umesh Kumar, Götz S. Uhrig, Benedikt Fauseweh

  • Stable bound quantum states are ubiquitous in nature. Mostly, they result from the interaction of only pairs of particles, so called two-body interactions, even when large complex many-particle structures are formed. We show that three-particle bound states occur in a generic, experimentally accessible solid state system: antiferromagnetic spin ladders, related to high-temperature superconductors. Strikingly, this binding is induced by genuine three-particle interactions; without them there is no bound state. We compute the dynamic exchange structure factor required for the experimental detection of the predicted state by resonant inelastic x-ray scattering for realistic material parameters. Our work enables us to quantify these elusive interactions and unambiguously establishes their effect on the dynamics of the quantum many-particle state. The data provided here contains the Green function and the weights of the dynamic exchange structure factor and the corresponding Lanczos coefficients.

Latest version: v1
Publication date: Dec 17, 2021

Crystal-graph attention networks for the prediction of stable materials


Jonathan Schmidt, Love Pettersson, Claudio Verdozzi, Silvana Botti, Miguel Marques

  • Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional ...

Latest version: v2
Publication date: Dec 16, 2021

SPAᴴM: the spectrum of approximated hamiltonian matrices representations


Alberto Fabrizio, Ksenia R. Briling, Clemence Corminboeuf

  • Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPAᴴM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAᴴM representations not only distinguish different molecules and ...

Latest version: v1
Publication date: Dec 15, 2021

Wigner formulation of thermal transport in solids


Michele Simoncelli, Nicola Marzari, Francesco Mauri

  • Two different heat-transport mechanisms are discussed in solids: in crystals, heat carriers propagate and scatter like particles, as described by Peierls' formulation of Boltzmann transport equation for phonon wavepackets; in glasses, instead, carriers behave wave-like, diffusing via a Zener-like tunneling between quasi-degenerate vibrational eigenstates, as described by the Allen-Feldman equation. Recently, it has been shown that these two conduction mechanisms emerge as limiting cases from a unified transport equation, which describes on an equal footing solids ranging from crystals to glasses; moreover, in materials with intermediate characteristics the two conduction mechanisms coexist, and it is crucial to account for both. Here, we discuss the theoretical foundations of such transport equation as is derived from the Wigner phase-space formulation of quantum mechanics, elucidating how the interplay between disorder, anharmonicity, and the quantum Bose-Einstein statistics of ...

Latest version: v1
Publication date: Dec 15, 2021

Multicellularity of delicate topological insulators


Aleksandra Nelson, Titus Neupert, Tomáš Bzdušek, Aris Alexandradinata

  • Being Wannierizable is not the end of the story for topological insulators. We introduce a family of topological insulators that would be considered trivial in the paradigm set by the tenfold way, topological quantum chemistry, and the method of symmetry-based indicators. Despite having a symmetric, exponentially localized Wannier representation, each Wannier function cannot be completely localized to a single primitive unit cell in the bulk. Such multicellular topology is shown to be neither stable nor fragile, but delicate; i.e., the topology can be nullified by adding trivial bands to either valence or conduction band.

Latest version: v1
Publication date: Dec 13, 2021

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression


Behnam Parsaeifard, Daniele Tomerini, Deb Sankar De, Stefan Goedecker

  • Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

Latest version: v1
Publication date: Dec 13, 2021

Equivariant representations for molecular Hamiltonians


Jigyasa Nigam, Michael J. Willatt, Michele Ceriotti

  • The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A major factor in this success has been the development of representations of atomic structures that reflect physics-based symmetries of the underlying interactions. Most of the descriptions of atomic properties or even global observables rely on decompositions into atomic contributions that are subsequently learnt in an atom-centered framework. However, many quantities associated with quantum mechanical calculations, such as the single-particle Hamiltonian matrices written in an atomic-orbital basis, are associated with multiple atom-centers. Following the introduction of equivariant N-center structural descriptors, in the reference below, that generalize the very successful atom-centered density correlation features to the problem of learning properties indexed by N atoms, we present ...

Latest version: v1
Publication date: Dec 09, 2021

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