Identification of potential solid-state Li-ion conductors with semi-supervised learning-Reference-Cited by-同舟云学术

Identification of potential solid-state Li-ion conductors with semi-supervised learning

Published:2023 Issue:3 Volume:16 Page:1264-1276
ISSN:1754-5692
Container-title:Energy & Environmental Science
language:en
Short-container-title:Energy Environ. Sci.

Author:

Laskowski Forrest A. L.¹,McHaffie Daniel B.²,See Kimberly A.¹^ORCID

Affiliation:

1. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA

2. Department of Applied Physics and Materials Science, California Institute of Technology, Pasadena, California 91125, USA

Abstract

A semi-supervised machine learning pipeline is reported for the discovery of new Li-ion solid-state electrolytes. The approach is experimentally validated with the synthesis and characterization of a new superionic conductor predicted by the model.

Funder

Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology

Arnold and Mabel Beckman Foundation

David and Lucile Packard Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/EE/D2EE03499A

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