TY - JOUR
T1 - Artificial Intelligence Applied to Battery Research
T2 - Hype or Reality?
AU - Lombardo, Teo
AU - Duquesnoy, Marc
AU - El-Bouysidy, Hassna
AU - Årén, Fabian
AU - Gallo-Bueno, Alfonso
AU - Jørgensen, Peter Bjørn
AU - Bhowmik, Arghya
AU - Demortière, Arnaud
AU - Ayerbe, Elixabete
AU - Alcaide, Francisco
AU - Reynaud, Marine
AU - Carrasco, Javier
AU - Grimaud, Alexis
AU - Zhang, Chao
AU - Vegge, Tejs
AU - Johansson, Patrik
AU - Franco, Alejandro A.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1021/acs.chemrev.1c00108
DO - 10.1021/acs.chemrev.1c00108
M3 - Review
C2 - 34529918
SN - 0009-2665
VL - 122
SP - 10899
EP - 10969
JO - Chemical Reviews
JF - Chemical Reviews
ER -