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
Y1 - 2023
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 - Electrochemical interface
KW - Density-functional theory
KW - Molecular dynamics simulation
KW - Electrochemical energy storage
KW - Machine learning
KW - Electrocatalysis
U2 - 10.1088/2515-7655/acfe9b
DO - 10.1088/2515-7655/acfe9b
M3 - Journal article
SN - 2515-7655
VL - 5
JO - Journal of Physics: Energy
JF - Journal of Physics: Energy
M1 - 041501
ER -