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Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Cameron Owen1*, Yu Xie2*, Anders Johansson2*, Lixin Sun2*, Boris Kozinsky2,3*

1 Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA

2 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA

3 Robert Bosch LLC, Research and Technology Center, Watertown, MA 02472, USA

* Corresponding authors emails: cowen@g.harvard.edu, xiey@g.harvard.edu, andersjohansson@g.harvard.edu, lixinsun@microsoft.com, bkoz@g.harvard.edu
DOI10.24435/materialscloud:va-hx [version v1]

Publication date: Feb 29, 2024

How to cite this record

Cameron Owen, Yu Xie, Anders Johansson, Lixin Sun, Boris Kozinsky, Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields, Materials Cloud Archive 2024.39 (2024), https://doi.org/10.24435/materialscloud:va-hx


Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-`Herringbone,' Au(110)-(1x2)-`Missing-Row,' and Au(100)-`Quasi-Hexagonal' reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies. The training data, MLFF, and subsequent simulations are provided for reproducibility.

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File name Size Description
9.5 MiB Directory containing all data and scripts to train the MLFF, rescale the energy noise, and map onto the final coefficient file for use in MD.
477 Bytes README file containing information for all directories.
2.4 GiB Directory containing simulation input and output, the latter in the form of lammps-data files. Trajectory files are not included to conserve memory, but the *.dat files corresponding to various snapshots along the simulation time as described in each input file.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Preprint where the data workflow and simulations are discussed.)


machine learning density-functional theory molecular dynamics simulation

Version history:

2024.39 (version v1) [This version] Feb 29, 2024 DOI10.24435/materialscloud:va-hx