Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials-Reference-Cited by-同舟云学术

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Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

Published:2018-11-21 Issue:2 Volume:31 Page:342-352
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Sendek Austin D.¹^ORCID,Cubuk Ekin D.²³,Antoniuk Evan R.⁴^ORCID,Cheon Gowoon¹^ORCID,Cui Yi²^ORCID,Reed Evan J.²

Affiliation:

1. Department of Applied Physics, Stanford University, Stanford, California 94305, United States

2. Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States

3. Google Brain, Mountain View, California 94043, United States

4. Department of Chemistry, Stanford University, Stanford, California 94305, United States

Funder

TomKat Center for Sustainable Energy, Stanford University

Toyota Research Institute

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.8b03272

Reference44 articles.

1. Recent results on lithium ion conductors

2. Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors

3. Li3Y(PS4)2 and Li5PS4Cl2: New Lithium Superionic Conductors Predicted from Silver Thiophosphates using Efficiently Tiered Ab Initio Molecular Dynamics Simulations

4. A lithium superionic conductor

5. Crystal structure of a superionic conductor, Li7P3S11

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