Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes-Reference-Cited by-同舟云学术

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Published:2022 Issue:28 Volume:10 Page:15169-15182
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Kim Kwangnam¹^ORCID,Siegel Donald J.¹²³⁴⁵

Affiliation:

1. Mechanical Engineering Department, University of Michigan, Ann Arbor, Michigan 48109-2125, USA

2. Materials Science & Engineering, University of Michigan, Ann Arbor, Michigan 48109-2125, USA

3. Applied Physics Program, University of Michigan, Ann Arbor, Michigan 48109-2125, USA

4. University of Michigan Energy Institute, University of Michigan, Ann Arbor, Michigan 48109-2125, USA

5. Joint Center for Energy Storage Research, University of Michigan, Ann Arbor, Michigan 48109-2125, USA

Abstract

Machine learning is used to identify and assess the relative importance of features that control ion mobility in anti-perovskite solid electrolytes. Lattice properties such as hopping distance and channel width have the largest impact.

Funder

Basic Energy Sciences

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/D2TA03613D

Reference90 articles.

1. Challenges for Rechargeable Li Batteries

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