Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session T00: Poster Session III (1pm- 4pm CST)
1:00 PM,
Thursday, March 17, 2022
Room: McCormick Place Exhibit Hall F1
Abstract: T00.00149 : Reconstruction of low-index Au surfaces using large-scale machine learning molecular dynamicswith many-body Bayesian force fields*
Presenter:
Cameron J Owen
(Harvard University)
Authors:
Cameron J Owen
(Harvard University)
Lixin Sun
(Harvard University)
Yu Xie
(Harvard University)
Boris Kozinsky
(Harvard University)
This work demonstrates a robust many-body Bayesian potential trained with an on-the-fly active learning framework implemented using Gaussian process regression in the FLARE code [1]. The active learning module uses molecular dynamics (MD) simulations of low-index surfaces to sample important configurations and runs density functional theory (DFT) calculations when the prediction Bayesian uncertainty exceeds a threshold. The trained Bayesian force field is then used to perform a large-scale MD to study surface diffusion and reconstructions of various surface facets, e.g., Herringbone reconstruction on (111) and missing-row reconstruction on (110).
[1] J. Vandermause et al, NPJ Computational Materials 6 (2020).
*C.J.O. is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. (DGE1745303). C.J.O used the Odyssey cluster, FAS Division of Science, Research Computing Group at Harvard University. L.S. acknowledges support from the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES) under Award No. DESC0012573. Y.X. is supported by the US Department of Energy (DOE) Office of Basic Energy Sciences under Award No. DE-SC0020128.
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