Towards predictive design of electrolyte solutions by accelerating <i>ab initio</i> simulation with neural networks-Reference-Cited by-同舟云学术

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

Published:2022 Issue:37 Volume:10 Page:19560-19571
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Zhang Junji¹^ORCID,Pagotto Joshua¹,Duignan Timothy T.¹²^ORCID

Affiliation:

1. School of Chemical Engineering, The University of Queensland, St Lucia, Brisbane 4072, Australia

2. School of Environment and Science, Griffith University, Nathan, Brisbane 4111, Australia

Abstract

Ab initio molecular dynamics can be massively accelerated using equivariant neural networks applicable to predict the properties of electrolyte solutions for predictive design in materials applications.

Funder

Australian Research Council

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TA/D2TA02610D

Reference128 articles.

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2. Sodium-Ion Battery Electrolytes: Modeling and Simulations

3. Kinetics of reaction between carbon dioxide and hydroxyl ions in aqueous electrolyte solutions

4. A Process for Capturing CO2 from the Atmosphere

5. Promoter Effects of Alkali Metal Cations on the Electrochemical Reduction of Carbon Dioxide

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