Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network-Reference-Cited by-同舟云学术

Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network

Published:2022-06-23 Issue:7 Volume:5 Page:8042-8048
ISSN:2574-0962
Container-title:ACS Applied Energy Materials
language:en
Short-container-title:ACS Appl. Energy Mater.

Author:

Lu Zhuole¹,Adeli Parvin²^ORCID,Yim Chae-Ho²^ORCID,Jiang Ming¹,Rempel Jacob¹,Chen Zhi Wen¹,Yadav Shwetank¹^ORCID,Mercier Patrick²,Abu-Lebdeh Yaser²^ORCID,Singh Chandra Veer¹³^ORCID

Affiliation:

1. Department of Materials Science and Engineering, University of Toronto, 184 College St., Toronto, Ontario M5S 3E4, Canada

2. Energy, Mining, and Environment Research Centre, National Research Council of Canada, 1200 Montreal Road, Ottawa, Ontario K1A 0R6, Canada

3. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada

Funder

Government of Canada

University of Toronto

Canada Foundation for Innovation

Publisher

American Chemical Society (ACS)

Subject

Electrical and Electronic Engineering,Materials Chemistry,Electrochemistry,Energy Engineering and Power Technology,Chemical Engineering (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsaem.2c00493

Reference44 articles.

1. Designing solid-state electrolytes for safe, energy-dense batteries

2. Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes

3. Stable Lithium Ion Conducting Thiophosphate Solid Electrolytes Lix(PS4)yXz (X = Cl, Br, I)

4. High Throughput Methodology for Synthesis, Screening, and Optimization of Solid State Lithium Ion Electrolytes

5. High-throughput computational screening for solid-state Li-ion conductors

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