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Huazhong University of Science and Technology, China.
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2023 (English)In: Journal of Physics: Energy, E-ISSN 2515-7655, Vol. 5, no 4, article id 041501Article, review/survey (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2023
Keywords
electrochemical interfaces, density-functional theory, molecular dynamics simulation, electrochemical energy storage, machine learning, electrocatalysis
National Category
Energy Engineering
Research subject
Materials Science
Identifiers
urn:nbn:se:kau:diva-97389 (URN)10.1088/2515-7655/acfe9b (DOI)001090149100001 ()2-s2.0-85177181901 (Scopus ID)
Funder
Swedish Energy Agency, P50638-1EU, Horizon 2020, 771294, 851441, 957189, 949012VinnovaKnut and Alice Wallenberg FoundationAcademy of Finland, 338228
2023-11-162023-11-162023-12-04Bibliographically approved