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2023 Roadmap on molecular modelling of electrochemical energy materials

Name: 2023 Roadmap on molecular modelling of electrochemical energy materials
Keywords: TK1001-1841, Electrochemical Interfaces, TJ807-830, Molecular Dynamics Simulation, 530, Renewable energy sources, Machine Learning, Production of electric energy or power. Powerplants. Central stations, Density-Functional Theory, electrocatalysis

descriptionPublicationkeyboard_double_arrow_right Article 01 Oct 2023 France, Switzerland Publisher:IOP PublishingJournal:Journal of Physics: Energy, volume 5, page 41,501 (eissn: 2515-7655,

Authors: Zhang, Chao; Cheng, Jun; Chen, Yiming; Chan, Maria K. Y.; Cai, Qiong; Carvalho, Rodrigo P.; Marchiori, Cleber F. N.; +25 Authors

doi: 10.1088/2515-7655/acfe9b , 10.18154/rwth-2023-10624

2023 Roadmap on molecular modelling of electrochemical energy materials

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Abstract

AbstractNew 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, CO2reduction reaction, and fuel cell applications.

Countries

France, Switzerland

Related Organizations

University of Jyväskylä
Finland
French National Centre for Scientific Research
France
RWTH Aachen University
Germany
University of Limoges
France
Institut Universitaire de France
France

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Keywords

TK1001-1841, Electrochemical Interfaces, TJ807-830, Molecular Dynamics Simulation, 530, Renewable energy sources, Machine Learning, Production of electric energy or power. Powerplants. Central stations, Density-Functional Theory, electrocatalysis, electrochemical interfaces, density-functional theory, electrochemical energy storage, [CHIM.MATE]Chemical Sciences/Material chemistry, [CHIM.CATA]Chemical Sciences/Catalysis, 541, [CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry, molecular dynamics simulation, machine learning, Electrochemical Energy Storage, Electrocatalysis

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	12
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%