Artificial Intelligence Applied to Battery Research: Hype or Reality?

Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, Alfonso Gallo-Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco*

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

194 Downloads (Pure)


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.
Original languageEnglish
JournalChemical Reviews
Pages (from-to)10899–10969
Number of pages71
Publication statusPublished - 2022


Dive into the research topics of 'Artificial Intelligence Applied to Battery Research: Hype or Reality?'. Together they form a unique fingerprint.

Cite this