Molecular dynamics-machine learning approaches for the accurate prediction of electrochemical windows of ionic liquid electrolytes for dual-ion batteries-Reference-Cited by-同舟云学术

Molecular dynamics-machine learning approaches for the accurate prediction of electrochemical windows of ionic liquid electrolytes for dual-ion batteries

Published:2023 Issue:40 Volume:11 Page:21702-21712
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Manna Surya Sekhar¹^ORCID,Manna Souvik¹^ORCID,Pathak Biswarup¹^ORCID

Affiliation:

1. Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India

Abstract

We have considered different cation and anion based ionic liquids and predicted the electrochemical window of 660 ionic liquid-based electrolytes using machine learning techniques for dual ion battery studies.

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Council of Scientific and Industrial Research, India

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/2023/TA/D3TA04310J

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1. Electrical Energy Storage and Intercalation Chemistry

2. Controversial Topics on Lithium Superoxide in Li–O2 Batteries