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

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 QiuHai 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

Research output: Contribution to journal › Review article › peer-review

11 Citations (Scopus)

Abstract

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, 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, CO₂ reduction reaction, and fuel cell applications.

Original language	English
Article number	041501
Journal	JPhys Energy
Volume	5
Issue number	4
DOIs	https://doi.org/10.1088/2515-7655/acfe9b
Publication status	Published - 1 Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.

Keywords

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

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10.1088/2515-7655/acfe9b

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Zhang, C., Cheng, J., Chen, Y., Chan, M. K. Y., Cai, Q., Carvalho, R. P., Marchiori, C. F. N., Brandell, D., Araujo, C. M., Chen, M., Ji, X., Feng, G., Goloviznina, K., Serva, A., Salanne, M., Mandai, T., Hosaka, T., Alhanash, M., Johansson, P., ... Sundararaman, R. (2023). 2023 Roadmap on molecular modelling of electrochemical energy materials. JPhys Energy, 5(4), Article 041501. https://doi.org/10.1088/2515-7655/acfe9b

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abstract = "New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, 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, CO2 reduction reaction, and fuel cell applications.",

keywords = "density-functional theory, electrocatalysis, electrochemical energy storage, electrochemical interfaces, machine learning, molecular dynamics simulation",

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

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by IOP Publishing Ltd.",

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doi = "10.1088/2515-7655/acfe9b",

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Zhang, C, Cheng, J, Chen, Y, Chan, MKY, Cai, Q, Carvalho, RP, Marchiori, CFN, Brandell, D, Araujo, CM, Chen, M, Ji, X, Feng, G, Goloviznina, K, Serva, A, Salanne, M, Mandai, T, Hosaka, T, Alhanash, M, Johansson, P, Qiu, YZ, Xiao, H, Eikerling, M, Jinnouchi, R, Melander, MM, Kastlunger, G, Bouzid, A, Pasquarello, A, Shin, SJ, Kim, MM, Kim, H, Schwarz, K & Sundararaman, R 2023, '2023 Roadmap on molecular modelling of electrochemical energy materials', JPhys Energy, vol. 5, no. 4, 041501. https://doi.org/10.1088/2515-7655/acfe9b

TY - JOUR

T1 - 2023 Roadmap on molecular modelling of electrochemical energy materials

AU - Zhang, Chao

AU - Cheng, Jun

AU - Chen, Yiming

AU - Chan, Maria K.Y.

AU - Cai, Qiong

AU - Carvalho, Rodrigo P.

AU - Marchiori, Cleber F.N.

AU - Brandell, Daniel

AU - Araujo, C. Moyses

AU - Chen, Ming

AU - Ji, Xiangyu

AU - Feng, Guang

AU - Goloviznina, Kateryna

AU - Serva, Alessandra

AU - Salanne, Mathieu

AU - Mandai, Toshihiko

AU - Hosaka, Tomooki

AU - Alhanash, Mirna

AU - Johansson, Patrik

AU - Qiu, Yun Ze

AU - Xiao, Hai

AU - Eikerling, Michael

AU - Jinnouchi, Ryosuke

AU - Melander, Marko M.

AU - Kastlunger, Georg

AU - Bouzid, Assil

AU - Pasquarello, Alfredo

AU - Shin, Seung Jae

AU - Kim, Minho M.

AU - Kim, Hyungjun

AU - Schwarz, Kathleen

AU - Sundararaman, Ravishankar

PY - 2023/10/1

Y1 - 2023/10/1

N2 - New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, 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, CO2 reduction reaction, and fuel cell applications.

AB - New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, 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, CO2 reduction reaction, and fuel cell applications.

KW - density-functional theory

KW - electrocatalysis

KW - electrochemical energy storage

KW - electrochemical interfaces

KW - machine learning

KW - molecular dynamics simulation

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U2 - 10.1088/2515-7655/acfe9b

DO - 10.1088/2515-7655/acfe9b

M3 - Review article

AN - SCOPUS:85177181901

SN - 2515-7655

VL - 5

JO - JPhys Energy

JF - JPhys Energy

IS - 4

M1 - 041501

ER -

2023 Roadmap on molecular modelling of electrochemical energy materials

Abstract

Bibliographical note

Keywords

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