Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity-Reference-Cited by-同舟云学术

Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity

Published:2024 Issue:30 Volume:12 Page:19123-19136
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Yan Peng¹,Fischer Mirko²^ORCID,Martin Harrison²,Wölke Christian¹^ORCID,Krishnamoorthy Anand Narayanan¹,Cekic-Laskovic Isidora¹^ORCID,Diddens Diddo¹^ORCID,Winter Martin¹³^ORCID,Heuer Andreas²^ORCID

Affiliation:

1. Helmholtz-Institute Münster (IEK-12), Forschungszentrum Jülich GmbH, Corrensstraße 46, 48149 Münster, Germany

2. Institute of Physical Chemistry, University of Münster, Corrensstraße 28/30, 48149 Münster, Germany

3. MEET Battery Research Center, University of Münster, Corrensstraße 46, 48149 Münster, Germany

Abstract

We study the ionic conductivity of a bi-salt (LiPF6, LiFSI) and ternary solvent (EC, EMC, PC) liquid battery electrolyte with high throughput experimentation and the open source Liquid Electrolyte Composition Analysis (LECA) Machine-Learning library.

Funder

European Commission

Bundesministerium für Bildung und Forschung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06249J

Reference49 articles.

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