Toward Automated Computational Discovery of Battery Materials

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Toward Automated Computational Discovery of Battery Materials
Title:
Toward Automated Computational Discovery of Battery Materials
Other Titles:
Advanced Materials Technologies
Publication Date:
05 July 2022
Citation:
Feng, X., Zhang, Q., & Seh, Z. W. (2022). Toward Automated Computational Discovery of Battery Materials. Advanced Materials Technologies, 2200616. Portico. https://doi.org/10.1002/admt.202200616
Abstract:
New rechargeable batteries with high energy density and low cost have been intensively explored, but their commercialization still faces multiple challenges involving battery materials and interfaces. Some difficulties faced by battery materials are that a single material often needs to possess multiple functions, and also needs to be cheap, easy to prepare, safe, and environmentally friendly. Recent developments in workflow managers (WMs) along with continuously increasing computing power have enabled the automated computational workflow method. Using this method, the WM can execute the predesigned research workflow to study tens of thousands of materials and screen out materials that meet the multiple requirements. In this perspective, a critical overview of the automated computational workflows is presented, focusing on the high-throughput study of battery materials. First, an introduction to the automated computational workflow as well as commonly used WMs will be given. Next, the latest works and methods to build such automated workflows are presented. Finally, an outlook on the existing challenges and future directions to drive computational and experimental developments in this nascent field is provided.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF2017-04

This research is supported by core funding from: SERC
Grant Reference no. : NA

Beijing Natural Science Foundation (2192029).
Description:
This is the peer reviewed version of the following article: Feng, X., Zhang, Q., & Seh, Z. W. (2022). Toward Automated Computational Discovery of Battery Materials. Advanced Materials Technologies, 2200616. Portico. https://doi.org/10.1002/admt.202200616, which has been published in final form atdoi.org/10.1002/admt.202200616. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
ISSN:
2365-709X
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