Constant-Potential Machine Learning Force Field for the Electrochemical Interface

Ruoyu Wang; Shaoheng Fang; Qixing Huang; Yuanyue Liu

doi:10.1021/acs.jctc.5c00784

journal article Jul 28, 2025

Constant-Potential Machine Learning Force Field for the Electrochemical Interface

Ruoyu Wang

Shaoheng Fang Qixing Huang Yuanyue Liu

Journal of Chemical Theory and Computation Vol. 21 No. 15 pp. 7628-7635 · American Chemical Society (ACS)

View at Publisher Save 10.1021/acs.jctc.5c00784

Topics

No keywords indexed for this article. Browse by subject →

References

48

[1]

10.1016/s0167-5729(01)00022-x

[2]

A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels

Jinli Qiao, Yuyu Liu, Feng Hong et al.

Chemical Society Reviews 10.1039/c3cs60323g

[3]

10.1038/s41570-016-0003

[4]

Combining theory and experiment in electrocatalysis: Insights into materials design

Zhi Wei Seh, Jakob Kibsgaard, Colin F. Dickens et al.

Science 10.1126/science.aad4998

[5]

10.1038/nmat4738

[6]

10.1021/acs.chemrev.3c00735

[7]

10.1038/nchem.121

[8]

10.1021/jp047349j

[9]

10.1016/j.cplett.2008.10.024

[10]

10.1016/j.surfrep.2020.100492

[11]

10.1021/acs.chemrev.1c00981

[12]

10.1021/jacs.8b13188

[13]

10.1021/acs.chemrev.1c00679

[14]

10.1146/annurev-physchem-090519-024042

[15]

10.1021/acs.jpcc.6b10908

[16]

10.1021/acs.chemmater.9b01294

[17]

10.1021/acs.jpclett.1c01204

[18]

10.1063/1.5019779

[19]

10.1021/acs.chemrev.0c01111

[20]

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Haitao Wang, Linfeng Zhang, Jiequn Han et al.

Computer Physics Communications 10.1016/j.cpc.2018.03.016

[21]

10.1103/physrevlett.109.266101

[22]

10.1063/1.5054580

[23]

10.1103/physrevb.73.165402

[24]

10.1103/physrevlett.126.136803

[25]

10.1103/physrevlett.120.246801

[26]

10.1103/physrevlett.131.238003

[27]

10.1063/1.4978411

[28]

Grand Canonical Ensemble Modeling of Electrochemical Interfaces Made Simple

Zhaoming Xia, Hai Xiao

Journal of Chemical Theory and Computation 10.1021/acs.jctc.3c00237

[29]

10.1016/j.jcat.2020.08.032

[30]

10.1021/jacs.3c09193

[31]

10.1021/jacs.1c02186

[32]

10.1021/acs.jctc.9b00633

[33]

10.1021/acs.jpclett.8b00573

[34]

10.1016/j.jcat.2019.05.025

[35]

Chen L. arXiv (2024)

[36]

10.1038/s41524-023-01007-6

[37]

10.1038/s41467-025-58871-7

[38]

10.1021/acs.jpcc.4c08188

[39]

The design space of E(3)-equivariant atom-centred interatomic potentials

Ilyes Batatia, Simon Batzner, Dávid Péter Kovács et al.

Nature Machine Intelligence 10.1038/s42256-024-00956-x

[40]

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner, Albert Musaelian, Lixin Sun et al.

Nature Communications 10.1038/s41467-022-29939-5

[41]

Satorras V. C. G. (2021)

[42]

Schütt K. (2021)

[43]

Batatia I. (2022)

[44]

10.1002/adma.201706617

[45]

10.1021/jacs.7b09074

[46]

10.1002/anie.201911995

[47]

10.1021/jacs.9b13872

[48]

10.1002/anie.202413614

Metrics

15

Citations

48

References

Details

Published: Jul 28, 2025
Vol/Issue: 21(15)
Pages: 7628-7635
License: View

Authors

R

Ruoyu Wang

Texas Materials Institute and Department of Mechanical Engineering; The University of Texas at Austin

S

Shaoheng Fang

Department of Computer Science; The University of Texas at Austin

Q

Qixing Huang

Department of Computer Science; The University of Texas at Austin

Y

Yuanyue Liu

Texas Materials Institute and Department of Mechanical Engineering; The University of Texas at Austin

Funding

National Science Foundation Award: 2430894

Welch Foundation Award: F-1959

U.S. Department of Defense Award: HR0011-24-2-0380

Cite This Article

Ruoyu Wang, Shaoheng Fang, Qixing Huang, et al. (2025). Constant-Potential Machine Learning Force Field for the Electrochemical Interface. Journal of Chemical Theory and Computation, 21(15), 7628-7635. https://doi.org/10.1021/acs.jctc.5c00784

Constant-Potential Machine Learning Force Field for the Electrochemical Interface

You May Also Like