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

Research output: Contribution to journalJournal articleResearchpeer-review

55 Downloads (Pure)

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 languageEnglish
Article number041501
JournalJournal of Physics: Energy
Volume5
Number of pages56
ISSN2515-7655
DOIs
Publication statusPublished - 2023

Keywords

  • Electrochemical interface
  • Density-functional theory
  • Molecular dynamics simulation
  • Electrochemical energy storage
  • Machine learning
  • Electrocatalysis

Fingerprint

Dive into the research topics of '2023 Roadmap on molecular modelling of electrochemical energy materials'. Together they form a unique fingerprint.

Cite this