Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics-Reference-Cited by-同舟云学术

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Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics

Published:2024-04-24 Issue:21 Volume:146 Page:14566-14575
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Wang Feng¹,Ma Zebing¹,Cheng Jun¹²³^ORCID

Affiliation:

1. State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China

2. Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen 361005, China

3. Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China

Funder

National Natural Science Foundation of China

Laboratory of AI for Electrochemistry (AI4EC)

Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen University

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.4c01221

Reference89 articles.

1. Review of electrical energy storage technologies, materials and systems: challenges and prospects for large-scale grid storage

2. The path towards sustainable energy

3. Electrochemical Energy Storage for Green Grid

4. Review of the Development of First‐Generation Redox Flow Batteries: Iron‐Chromium System

5. The Chemistry of Redox‐Flow Batteries

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