2023 Roadmap on molecular modelling of electrochemical energy materials

Chao Zhang; Jun Cheng; Yiming Chen; Maria K Y Chan; Qiong Cai; Rodrigo P Carvalho; Cleber F N Marchiori; Daniel Brandell; C Moyses Araujo; Ming Chen; Xiangyu Ji; Guang Feng; Kateryna Goloviznina; Alessandra Serva; Mathieu Salanne; Toshihiko Mandai; Tomooki Hosaka; Mirna Alhanash; Patrik Johansson; Yun-Ze Qiu; Hai Xiao; Michael Eikerling; Ryosuke Jinnouchi; Marko M Melander; Georg Kastlunger; Assil Bouzid; Alfredo Pasquarello; Seung-Jae Shin; Minho M Kim; Hyungjun Kim; Kathleen Schwarz; Ravishankar Sundararaman

doi:10.1088/2515-7655/acfe9b

Journal article 2023 Roadmap on molecular modelling of electrochemical energy materials

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74_Roadmap_J Phys Energy 2023_Roadmap on molecular modelling of electrochemical energy materials.pdf

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Chao Zhang, Jun Cheng, Yiming Chen, Maria K Y Chan, Qiong Cai, Rodrigo P Carvalho, Cleber F N Marchiori, Daniel Brandell, C Moyses Araujo, Ming Chen, Xiangyu Ji, Guang Feng, Kateryna Goloviznina, Alessandra Serva, Mathieu Salanne, Toshihiko Mandai, Tomooki Hosaka, Mirna Alhanash, Patrik Johansson, Yun-Ze Qiu, Hai Xiao, Michael Eikerling, Ryosuke Jinnouchi, Marko M Melander, Georg Kastlunger, Assil Bouzid, Alfredo Pasquarello, Seung-Jae Shin, Minho M Kim, Hyungjun Kim, Kathleen Schwarz, Ravishankar Sundararaman. 2023 Roadmap on molecular modelling of electrochemical energy materials. Journal of Physics-Energy. 2023, 5 (4), 41501-41501. https://doi.org/10.1088/2515-7655/acfe9b

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(abstract)

Overall, this roadmap originating from 20 groups in 11 countries serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO2RR, and fuel cell applications. It is not intended to be a comprehensive review, rather they are opinions of leading experts in their respective domains. Therefore, topics such as solid electrolytes (both ceramic and polymer), descriptor engineering of electrocatalysts and their ML discovery, are not included in this collection and can be found elsewhere.

Rights:

Creative Commons BY Attribution 4.0 International

Keyword: electrochemical interfaces, density-functional theory, molecular dynamics simulation, electrochemical energy storage, machine learning, electrocatalysis

Date published: 2023-10-01

Publisher: IOP Publishing

Journal:

Journal of Physics-Energy (ISSN: 25157655) vol. 5 issue. 4 p. 41501-41501

Funding:

JSPS JP21K05263 (リチウムおよびナトリウム系電解質の電気化学安定性を支配する制御因子の究明)
JST JPMJPF2016 (先進蓄電池研究開発拠点)

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1088/2515-7655/acfe9b

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Updated at: 2024-03-22 00:53:39 +0900

Published on MDR: 2024-03-05 16:30:28 +0900

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