2023 Roadmap on molecular modelling of electrochemical energy materials

JPhys Energy(2023)

引用 0|浏览21
暂无评分
摘要
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, CO _2 reduction reaction, and fuel cell applications.
更多
查看译文
关键词
electrochemical interfaces, density-functional theory, molecular dynamics simulation, electrochemical energy storage, machine learning, electrocatalysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要