2023 Roadmap on molecular modelling of electrochemical energy materials
Source abstract
Abstract
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, CO2reduction reaction, and fuel cell applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Fuel Cells and Related Materials · Electrocatalysts for Energy Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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