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Title: Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions

Abstract

While the ab initio molecular dynamics (AIMD) approach to gas–surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is orders-of-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/reaction of the impinging molecule on the solid surface on an equal footing.

Authors:
 [1];  [1];  [2];  [1];  [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
  2. Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center
Sponsoring Org.:
USDOE
OSTI Identifier:
1483684
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Volume: 122; Journal Issue: 3; Journal ID: ISSN 1932-7447
Country of Publication:
United States
Language:
English

Citation Formats

Liu, Qinghua, Zhou, Xueyao, Zhou, Linsen, Zhang, Yaolong, Luo, Xuan, Guo, Hua, and Jiang, Bin. Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions. United States: N. p., 2018. Web. doi:10.1021/acs.jpcc.7b12064.
Liu, Qinghua, Zhou, Xueyao, Zhou, Linsen, Zhang, Yaolong, Luo, Xuan, Guo, Hua, & Jiang, Bin. Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions. United States. doi:10.1021/acs.jpcc.7b12064.
Liu, Qinghua, Zhou, Xueyao, Zhou, Linsen, Zhang, Yaolong, Luo, Xuan, Guo, Hua, and Jiang, Bin. Thu . "Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions". United States. doi:10.1021/acs.jpcc.7b12064. https://www.osti.gov/servlets/purl/1483684.
@article{osti_1483684,
title = {Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions},
author = {Liu, Qinghua and Zhou, Xueyao and Zhou, Linsen and Zhang, Yaolong and Luo, Xuan and Guo, Hua and Jiang, Bin},
abstractNote = {While the ab initio molecular dynamics (AIMD) approach to gas–surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is orders-of-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/reaction of the impinging molecule on the solid surface on an equal footing.},
doi = {10.1021/acs.jpcc.7b12064},
journal = {Journal of Physical Chemistry. C},
number = 3,
volume = 122,
place = {United States},
year = {2018},
month = {1}
}

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