The Library
Symmetry-adapted high dimensional neural network representation of electronic friction tensor of adsorbates on metals
Tools
Zhang, Yaolong, Maurer, Reinhard J. and Jiang, Bin (2020) Symmetry-adapted high dimensional neural network representation of electronic friction tensor of adsorbates on metals. Journal of Physical Chemistry C, 124 (1). pp. 186-195. doi:10.1021/acs.jpcc.9b09965 ISSN 1932-7447.
|
PDF
WRAP-symmetry-adapted-high-dimensional-neural-network-representation-2010.pdf - Accepted Version - Requires a PDF viewer. Download (5Mb) | Preview |
Official URL: https://doi.org/10.1021/acs.jpcc.9b09965
Abstract
Nonadiabatic effects in chemical reaction at metal surfaces, due to excitation of electron–hole pairs, stand at the frontier of the studies of gas-surface reaction dynamics. However, the first-principles description of electronic excitation remains challenging. In an efficient molecular dynamics with electronic friction (MDEF) method, the nonadiabatic couplings are effectively included in a so-called electronic friction tensor (EFT), which can be computed from first-order time-dependent perturbation theory (TDPT) in terms of density functional theory (DFT) orbitals. This second-rank tensor depends on adsorbate position and features a complicated transformation with regard to the intrinsic symmetry operations of the system. In this work, we develop a new symmetry-adapted neural network representation of EFT, based on our recently proposed embedded atom neural network (EANN) framework. Inspired by the derivation of the nonadiabatic coupling matrix, we represent the tensorial friction by the first and second derivatives of multiple outputs of NNs with respect to atomic Cartesian coordinates. This rigorously preserves the positive semidefiniteness, directional property, and correct symmetry-equivariance of EFT. Unlike previous methods, our new approach can readily include both molecular and surface degrees of freedom, regardless of the type of surface. Tests on the H2 + Ag(111) system show that this approach yields an accurate, efficient, and continuous representation of EFT, making it possible to perform large scale TDPT-based MDEF simulations to study both adiabatic and nonadiabatic energy dissipation in a unified framework.
Item Type: | Journal Article | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QD Chemistry | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Density functionals , Surface chemistry, Metals -- Surfaces, Energy transfer | |||||||||||||||
Journal or Publication Title: | Journal of Physical Chemistry C | |||||||||||||||
Publisher: | American Chemical Society | |||||||||||||||
ISSN: | 1932-7447 | |||||||||||||||
Official Date: | 9 January 2020 | |||||||||||||||
Dates: |
|
|||||||||||||||
Volume: | 124 | |||||||||||||||
Number: | 1 | |||||||||||||||
Page Range: | pp. 186-195 | |||||||||||||||
DOI: | 10.1021/acs.jpcc.9b09965 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | “This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry C, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].” | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Copyright Holders: | Copyright © 2019 American Chemical Society | |||||||||||||||
Date of first compliant deposit: | 6 January 2021 | |||||||||||||||
Date of first compliant Open Access: | 6 January 2021 | |||||||||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year