CaltechAUTHORS
  A Caltech Library Service

Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts

Naserifar, Saber and Chen, Yalu and Kwon, Soonho and Xiao, Hai and Goddard, William A., III (2021) Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts. Matter, 4 (1). pp. 195-216. ISSN 2590-2385. https://resolver.caltech.edu/CaltechAUTHORS:20201204-185233484

[img] PDF (Supplemental Experimental Procedures, Figures S1–S10, and Tables S1 and S2) - Supplemental Material
See Usage Policy.

1MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20201204-185233484

Abstract

To develop new generations of electrocatalysts, we need the accuracy of full explicit solvent quantum mechanics (QM) for practical-sized nanoparticles and catalysts. To do this, we start with the RexPoN reactive force field that provides higher accuracy than density functional theory (DFT) for water and combine it with QM to accurately include long-range interactions and polarization effects to enable reactive simulations with QM accuracy in the presence of explicit solvent. We apply this RexPoN-embedded QM (ReQM) to reactive simulations of electrocatalysis, demonstrating that ReQM accurately replaces DFT water for computing the Raman frequencies of reaction intermediates during CO₂ reduction to ethylene. Then, we illustrate the power of this approach by combining with machine learning to predict the performance of about 10,000 surface sites and identify the active sites of solvated gold (Au) nanoparticles and dealloyed Au surfaces. This provides an accurate but practical way to design high-performance electrocatalysts.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.matt.2020.11.010DOIArticle
ORCID:
AuthorORCID
Naserifar, Saber0000-0002-1069-9789
Chen, Yalu0000-0002-0589-845X
Kwon, Soonho0000-0002-9225-3018
Xiao, Hai0000-0001-9399-1584
Goddard, William A., III0000-0003-0097-5716
Additional Information:© 2020 Published by Elsevier Inc. Received 17 May 2020, Revised 28 July 2020, Accepted 6 November 2020, Available online 27 November 2020. We thank the Joint Center for Artificial Photosynthesis (JCAP), a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under award number DE-SC0004993 and the Computational Materials Sciences Program funded by the US Department of Energy, Office of Science, Basic Energy Sciences, under award number DE-SC00014607. Although JCAP funded most of the calculations, the machine learning application was funded by the Liquid Sunlight Alliance (LiSA), a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under award number DE-SC0021266. The calculations were carried out on the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Author Contributions: S.N. and W.A.G. designed research. S.N. and Y.C. performed the calculations. S.N., Y.C., and W.A.G. analyzed data. S.N. and W.A.G. wrote the paper. S.K. and H.X. provided some data and programming scripts that were used in this research. The authors declare no competing interests.
Group:JCAP
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0004993
Department of Energy (DOE)DE-SC00014607
Department of Energy (DOE)DE-SC0021266
NSFACI-1548562
Subject Keywords:machine learning; quantum mechanics; polarizable reactive force field; explicit solvent; vibrational frequency; catalyst; electrocatalysis; nanoparticles
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1403
Issue or Number:1
Record Number:CaltechAUTHORS:20201204-185233484
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201204-185233484
Official Citation:Saber Naserifar, Yalu Chen, Soonho Kwon, Hai Xiao, William A. Goddard, Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts, Matter, Volume 4, Issue 1, 2021, Pages 195-216, ISSN 2590-2385, https://doi.org/10.1016/j.matt.2020.11.010. (http://www.sciencedirect.com/science/article/pii/S2590238520306275)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106934
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Dec 2020 18:34
Last Modified:27 Mar 2021 07:09

Repository Staff Only: item control page