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Ab initio thermodynamics of liquid and solid water: supplemental materials

[materialscloud:2018.0020] Last version: 04 December 2018

Bingqing Cheng, Edgar Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step. In this set of supplemental materials, we have included the neural network potential for ...


Special quasi-random structures for the 6-component high entropy alloys

[materialscloud:2018.0019] Last version: 03 December 2018

Binglun Yin, William Curtin

We propose a general method to calculate the average misfit volumes of atoms in any random alloy via DFT calculations. The method is validated with an example of a 6-component equi-composition high entropy alloy. The special quasi-random structures (SQSs) used in our work are reported here.


Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

[materialscloud:2018.0017] Last version: 29 November 2018

Daniele Ongari, Peter G. Boyd, Amber K. Mace, Berend Smit, Ozge Kadioglu, Seda Keskin

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in Metal Organic Frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality ...


Data-driven design and synthesis of metal-organic frameworks for wet flue gas CO2 capture

[materialscloud:2018.0016] Last version: 25 November 2018

Peter George Boyd, Arunraj Chidambaram, Thomas D. Daff, Richard Bounds, Andrzej Gładysiak, Pascal Schouwink, Seyed Mohamad Moosavi, Jeffrey A. Reimer, Jorge A. R. Navarro, Tom K. Woo, Berend Smit, Kyriakos C. Stylianou

In this entry is a database of 324,426 hypothetical Metal-Organic Frameworks (MOFs) that were used in a study to screen potential carbon dioxide scrubbers. Using a method to assemble these materials with topological blueprints, we only selected materials that could be accurately represented with the MEPO-QEq charge generation method. By ensuring that the electrostatic potential is accurately represented in these materials, screening for CO2 adsorption properties would result very few false positives/negatives. The atom-centered charges reported in the CIF file for each MOF were derived from the MEPO-QEq method, which can be found under the '_atom_type_partial_charge' column in each CIF file. The relevant data for each MOF is reported in accompanying .csv files. Post-combustion flue gas was simulated at a temperature of both 298K and 0.15 bar CO2, and 313K and 0.15 bar CO2. Mixture adsorption was simulated with the conditions 298K and 0.15:0.85 CO2/N2 with a total ...


Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111)

[materialscloud:2018.0018] Last version: 23 November 2018

Aliaksandr V. Yakutovich, Johannes Hoja, Daniele Passerone, Alexandre Tkatchenko, Carlo A. Pignedoli

We provide the input files to reproduce the data presented in the work: Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111) The files are subdivided in directories named after the figures/table of the manuscript A. V. Yakutovich, J. Hoja, D. Passerone, Alexandre Tkatchenko, C. A. Pignedoli J. Am. Chem. Soc., 140, 1401-1408 (2018) DOI: 10.1021/jacs.7b10980 In the work, we unravel the origin of the recently observed striking enantioselectivity of the PdGa(111) surface with respect to the adsorption of a small organic molecule, 9-ethynylphenanthrene, using first-principles calculations. It turns out that the key ingredient to understand the experimental evidence is the appropriate description of van der Waals interactions beyond the widely employed atomic pairwise approximation. A recently developed van der Waals-inclusive density functional method, which encompasses dielectric screening effects, reveals the origin of the experimentally ...


High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

[materialscloud:2018.0005] Last version: 14 November 2018

Efrem Braun, Alexander F. Zurhelle, Wouter Thijssen, Sondre Schnell, Li-Chiang Lin, Jihan Kim, Joshua A. Thompson, Berend Smit

With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Qst, CH4) is decreased independently of Qst, CO2, but a correlation that exists between Qst, CH4 and Qst, CO2 in all-silica zeolites leads to incongruity between the objectives of optimizing Qst, CO2 and minimizing Qst, CH4, rendering Qst, CH4 nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using ...


Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

[materialscloud:2018.0007] Last version: 14 November 2018

Richard L. Martin, Li-Chiang Lin, Kuldeep Jariwala, Berend Smit, Maciej Haranczyk

Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical MOF design, comprising selection of appropriate ligands, construction of 3D structure models, and structure relaxation methods. 116 MOF-5 analogues were designed and characterized in terms of geometric properties and simulated methane uptake at conditions relevant to vehicular storage applications. A strength of the presented approach is that all of the hypothesized MOFs are designed to be synthesizable utilizing ligands purchasable online. Version 2 includes the structures in CIF format.


The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials (Data Download)

[materialscloud:2017.0003] Last version: 10 November 2018

Matthew Witman, Sanliang Ling, Sudi Jawahery, Peter G. Boyd, Maciej Haranczyk, Ben Slater, Berend Smit

Project Abstract: For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that ...


A Standard Solid State Pseudopotentials (SSSP) library optimized for precision and efficiency (Version 1.1, data download)

[materialscloud:2018.0001] Last version: 08 November 2018

Gianluca Prandini, Antimo Marrazzo, Ivano E. Castelli, Nicolas Mounet, Nicola Marzari

Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and precision libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP precision library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.


In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

[materialscloud:2018.0003] Last version: 05 October 2018

Rocio Mercado, Rueih-Sheng Fu, Aliaksandr V. Yakutovich, Leopold Talirz, Maciej Haranczyk, Berend Smit

Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This structure has a predicted 65-bar DC of 216 v STP/v, greater than that of the best current methane storage material. We also predict other top performing materials, with 305 structures having DCs of over 190 v STP/v, and 34 of these having DCs of over 200 v STP/v. This archive entry contains the database of assembled COF structures (in CIF file format) together with all of their properties, which can be explored using interactive figures. Among the ...


Toward GW Calculations on Thousands of Atoms

[materialscloud:2018.0015] Last version: 28 September 2018

Jan Wilhelm, Dorothea Golze, Leopold Talirz, Jürg Hutter, Carlo Antonio Pignedoli

We provide the input files needed to reproduce the results of the article Toward GW Calculations on Thousands of Atoms J. Wilhelm, D. Golze, L. Talirz, J. Hutter, C. A. Pignedoli J. Phys. Chem. Lett. 9, 306–312 (2018) DOI:10.1021/acs.jpclett.7b02740 The GW approximation of many-body perturbation theory is an accurate method for computing electron addition and removal energies of molecules and solids. In a canonical implementation, however, its computational cost is in the system size N, which prohibits its application to many systems of interest. We present a full-frequency GW algorithm in a Gaussian-type basis, whose computational cost scales with N2 to N3. The implementation is optimized for massively parallel execution on state-of-the-art supercomputers and is suitable for nanostructures and molecules in the gas, liquid or condensed phase, using either pseudopotentials or all electrons. We validate the accuracy of the algorithm on the GW100 molecular test ...


The geometric blueprint of perovskites

[materialscloud:2018.0012] Last version: 03 September 2018

Marina R. Filip, Feliciano Giustino

Perovskite minerals form an essential component of the Earth’s mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the “no-rattling” principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.


Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

[materialscloud:2018.0014] Last version: 01 August 2018

Benjamin Meyer, Boodsarin Sawatlon, Stefan Niklaus Heinen, O. Anatole von Lilienfeld, Clémence Corminboeuf

The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reaction. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18,062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$/mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include earth abundant (Cu) transition metal with ...


Generating carbon schwarzites via zeolite-templating

[materialscloud:2018.0013] Last version: 31 July 2018

Efrem Braun, Yongjin Lee, Seyed Mohamad Moosavi, Senja Barthel, Rocio Mercado, Igor A. Baburin, Davide M. Proserpio, Berend Smit

Zeolite-templated carbons (ZTCs) comprise a relatively recent material class synthesized via the chemical vapor deposition of a carbon-containing precursor on a zeolite template, followed by the removal of the template. We have developed a theoretical framework to generate a ZTC model from any given zeolite structure, which we show can successfully predict the structure of known ZTCs. We use our method to generate a library of ZTCs from all known zeolites, to establish criteria for which zeolites can produce experimentally accessible ZTCs, and to identify over 10 ZTCs that have never before been synthesized. We show that ZTCs partition space into two disjoint labyrinths that can be described by a pair of interpenetrating nets. Since such a pair of nets also describes a triply periodic minimal surface (TPMS), our results establish the relationship between ZTCs and schwarzites—carbon materials with negative Gaussian curvature that resemble TPMSs—linking the research topics and ...


Synthesis of Metal-Organic Frameworks: capturing chemical intuition

[materialscloud:2018.0011] Last version: 14 July 2018

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit

We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.


Mapping uncharted territory in ice from zeolite networks to ice structures

[materialscloud:2018.0010] Last version: 19 May 2018

Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs

We report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy and Deem, and the International Zeolite Association database. All prior knowledge of ice is set aside and we introduce generalised convex hulls to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I to XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the sketch-map dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we ...


Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

[materialscloud:2018.0009] Last version: 19 May 2018

Andrea Grisafi, David M. Wilkins, Gabor Csányi, Michele Ceriotti

Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120, 036002 (2018). The archive entry contains files in extended-XYZ format including the structures and several tensorial properties: for the monomer, dimer and Zundel cation, the dipole moment, polarizability and first hyperpolarizability are included, and for bulk water the dipole moment, polarizability and dielectric tensor are given.


In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

[materialscloud:2018.0008] Last version: 15 May 2018

Richard L. Martin, Cory M. Simon, Berend Smit, Maciej Haranczyk

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are ...


Improving the Mechanical Stability of Metal-Organic Frameworks Using Chemical Caryatids

[materialscloud:2018.0004] Last version: 17 April 2018

Seyed Mohamad Moosavi, Peter G. Boyd, Lev Sarkisov, Berend Smit

Metal-organic frameworks (MOFs) have emerged as versatile materials for applications ranging from gas separation and storage, catalysis, and sensing. The attractive feature of MOFs is that by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. In most, if not all, of these applications one also needs a material that has a sufficient mechanical stability, but our understanding of how changes in the chemical structure influence mechanical stability is limited. In this work, we rationalize how the mechanical properties of MOFs are related to framework bonding topology and ligand structure. We illustrate that the functional groups on the organic ligands can either enhance the mechanical stability through formation of a secondary network of non-bonded interactions, or soften the material by destabilizing the bonded network of a MOF. In addition, we show that synergistic effect of the bonding network of the material and the ...


Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

[materialscloud:2017.0008] Last version: 21 March 2018

Nicolas Mounet, Marco Gibertini, Philippe Schwaller, Davide Campi, Andrius Merkys, Antimo Marrazzo, Thibault Sohier, Ivano E. Castelli, Andrea Cepellotti, Giovanni Pizzi, Nicola Marzari

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 ...


Adatom-Induced Local Melting

[materialscloud:2018.0002] Last version: 11 February 2018

Ngoc Linh Nguyen, Francesca Baletto, Nicola Marzari

We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a paradigmatic case of a non-premelting surface that nevertheless displays facile adatom diffusion with single and multiple exchange pathways. Here, a single adatom deposited on the surface is sufficient to nucleate a localized and diffusing liquid-like region that remains confined to the surface layer, but with an area that increases with temperature; in the absence of the adatom, the surface instead remains crystalline until reaching the bulk melting temperature.


Isobaric-Isothermal Monte Carlo Simulations of Bulk Liquid Water from MP2 and RPA Theory (MC Trajectories Data Download)

[materialscloud:2017.0007] Last version: 28 November 2017

Mauro Del Ben, Joost VandeVondele, Juerg Hutter

Methods based on the second order Møller–Plesset perturbation theory (MP2) and the Random Phase Approximation (RPA) have emerged as practicable and reliable approaches to improve the accuracy of density functional approximations for first principle atomistic simulations. Such approaches are in fact capable to account ab-initio for non-local dynamical electron correlation effects, which play a fundamental role, for example, in the description of non-bonded interactions. To assess the performance of MP2 and RPA for real applications, isobaric-isothermal Monte Carlo simulations have been performed to study the structural properties of bulk liquid water under ambient conditions. The choice of bulk liquid water as benchmark system is motivated by the complicated nature of the intermolecular interactions, where repulsion, polarization, hydrogen bonding and van der Waals forces play an important role and are particularly difficult to reproduce accurately in atomistic models. The results ...


Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

[materialscloud:2017.0006] Last version: 06 November 2017

Daniele Dragoni, Tom Daff, Gabor Csanyi, Nicola Marzari

Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are designed using functional forms driven by physical intuition and fitted to experimental or computational data. The moderate flexibility of these functional forms limits their ability to be systematically improved by increasing the fitting datasets; on the other hand, their qualitative description of the essential physical interactions ensures a modicum degree of transferability. Recently, a novel trend has emerged where potential-energy surfaces are represented by neural networks fitted on large numbers of first-principles calculations, thus maximizing flexibility but requiring extensive datasets to ensure transferability. Gaussian Approximation Potentials in particular are a novel class of potentials based on non-linear, non-parametric Gaussian-process regression. Here we generate a Gaussian Approximation ...


Accurate Characterization of the Pore Volume in Microporous Crystalline Materials (Data Download)

[materialscloud:2017.0005] Last version: 18 May 2017

Daniele Ongari, Peter G. Boyd, Senja Barthel, Matthew Witman, Maciej Haranczyk, Berend Smit

Project Abstract: Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable and it can also be obtained from the refined unit cell by a number of computational techniques. In this work we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium and probe center pore volume, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe accessible and occupiable pore volume. We show that unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.


Predicting Product Distribution of Propene Dimerization in Nanoporous Materials (Data Download)

[materialscloud:2017.0004] Last version: 05 May 2017

Yifei Michelle Liu, Berend Smit

Project abstract: In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin ...


Barcodes for nanoporous materials

[materialscloud:2017.0001] Last version: 14 March 2017

Yongjin Lee, Senja D. Barthel, Paweł Dłotko, S. Mohamad Moosavi, Kathryn Hess, Berend Smit

In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. Recently, we developed a pore recognition approach to quantify similarity of pore structures using topological data analysis. Barcodes generated with using this approach allow us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. This database has barcodes for zeolites, metal organic frameworks, and zeolitic imidazolate frameworks.