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

^*Corresponding author for this work

MRC - Materials Research Center

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

20 Scopus citations

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 (US)
Article number	041501
Journal	JPhys Energy
Volume	5
Issue number	4
DOIs	https://doi.org/10.1088/2515-7655/acfe9b
State	Published - Oct 1 2023

Funding

This work was supported by the Samsung Science and Technology Foundation under Project Number SSTF-BA2101-08. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 771294). It was supported by the French National Research Agency (Labex STORE-EX, Grant No. ANR-10-LABX-0076). The author acknowledges funding of research presented in this article from the Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V. (HGF), Program-oriented Funding (PoF IV), under the Research Program Materials and Technologies for the Energy Transition (MTET). P J acknowledges the support from the Swedish Energy Agency (Grant P50638-1) to M A, the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 957189 (BIG-MAP), a part of Battery 2030+, his Swedish Research Council’s Distinguished Professor Grant, and Sweden’s Innovation Agency (VINNOVA) through Battery Alliance Sweden (BASE). T M acknowledges financial support from the NEXT Center of Innovation Program (COI-NEXT, Grant Number JPMJPF2016) of the Japan Science and Technology Agency and a Grant-in-Aid for Scientific Research (KAKENHI, Grant Number 21K05263) of the Japan Society for the Promotion of Science (JSPS). T H thanks the JSPS for the support through Grant-in-Aid for Scientific Research (KAKENHI, Grant Number 22K14772). We are grateful to the financial support from National Natural Science Foundation of China (Nos. 22122304 and 92261111), Tsinghua University Dushi Program, National Key Research and Development Project (2022YFA1503000) and Tsinghua University Initiative Scientific Research Program (20221080 065). This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 949012). This work was partially supported by the Wallenberg Initiative Materials Science for Sustainability (WISE) funded by the Knut and Alice Wallenberg Foundation (KAW). J C is grateful for the funding support from the National Natural Science Foundation of China (Grant Nos. 21861132015, 21991151, 21991150 and 22021001). Ravishankar Sundararaman acknowledges support from the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award #DE-SC0022247 This work is supported by the U.S. Department of Energy (DOE) Office of Science Scientific User Facilities project titled ‘Integrated Platform for Multimodal Data Capture, Exploration and Discovery Driven by AI Tools’. M C acknowledges the support from the BES SUFD Early Career award. Work performed at the Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User Facility, was supported by the U.S. DOE, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. M M M acknowledges the funding by Academy of Finland (Project # 338228, CompEL). G K thanks the European Union’s Horizon 2020 research and innovation program (Grant # 851441, SELECTCO2) and the Villum foundation (Grant # 9455, V-Sustain). Both authors acknowledge the GPAW developers for their continuous efforts to maintain and improve GPAW.

Keywords

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

ASJC Scopus subject areas

Materials Science (miscellaneous)
General Energy
Materials Chemistry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1088/2515-7655/acfe9b

Cite this

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.",

year = "2023",

month = oct,

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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

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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

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ER -

2023 Roadmap on molecular modelling of electrochemical energy materials

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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