| Literature DB >> 34529918 |
Teo Lombardo1,2, Marc Duquesnoy1,2, Hassna El-Bouysidy1,3,4, Fabian Årén3,4, Alfonso Gallo-Bueno3,5, Peter Bjørn Jørgensen3,6, Arghya Bhowmik3,6, Arnaud Demortière1,2,3, Elixabete Ayerbe3,7, Francisco Alcaide3,7, Marine Reynaud3,5, Javier Carrasco3,5, Alexis Grimaud2,3,8, Chao Zhang3,9, Tejs Vegge3,6, Patrik Johansson3,4, Alejandro A Franco1,2,3,10.
Abstract
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.Entities:
Mesh：
Year: 2021 PMID： 34529918 PMCID： PMC9227745 DOI： 10.1021/acs.chemrev.1c00108
Source DB: PubMed Journal: Chem Rev ISSN： 0009-2665 Impact factor: 72.087