Artificial Intelligence Applied to Battery Research: Hype or Reality?
Reviewartikel, 2021

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.

Författare

Teo Lombardo

Université de Picardie Jules Verne

FR CNRS 3459

Marc Duquesnoy

Université de Picardie Jules Verne

FR CNRS 3459

Hassna El-Bouysidy

Université de Picardie Jules Verne

FR CNRS 3104

Fabian Årén

FR CNRS 3104

Chalmers, Fysik, Materialfysik

Alfonso Gallo-Bueno

Basque Research and Technology Alliance (BRTA)

FR CNRS 3104

Peter Bjørn Jørgensen

FR CNRS 3104

Danmarks Tekniske Universitet (DTU)

Arghya Bhowmik

FR CNRS 3104

Danmarks Tekniske Universitet (DTU)

Arnaud Demortière

FR CNRS 3459

Université de Picardie Jules Verne

FR CNRS 3104

Elixabete Ayerbe

FR CNRS 3104

Centro de Investigacion Tecnológica En Electroquimica

Francisco Alcaide

Centro de Investigacion Tecnológica En Electroquimica

FR CNRS 3104

Marine Reynaud

FR CNRS 3104

Basque Research and Technology Alliance (BRTA)

Javier Carrasco

Basque Research and Technology Alliance (BRTA)

FR CNRS 3104

Alexis Grimaud

FR CNRS 3104

Université Pierre et Marie Curie (UPMC)

Collège de France

FR CNRS 3459

Chao Zhang

Uppsala universitet

FR CNRS 3104

T. Vegge

Danmarks Tekniske Universitet (DTU)

FR CNRS 3104

Patrik Johansson

Chalmers, Fysik, Materialfysik

FR CNRS 3104

Alejandro A. Franco

Université de Picardie Jules Verne

FR CNRS 3459

FR CNRS 3104

Institut Universitaire de France

Chemical Reviews

0009-2665 (ISSN) 1520-6890 (eISSN)

Vol. In Press

Tillämpad AI för batteri F&U

VINNOVA (2020-02321), 2020-11-01 -- 2021-07-15.

Högkoncentrerade elektrolyter

Energimyndigheten (39909-1), 2015-02-01 -- 2019-09-30.

Styrkeområden

Transport

Energi

Materialvetenskap

Ämneskategorier

Design

Systemvetenskap

Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

DOI

10.1021/acs.chemrev.1c00108

Mer information

Senast uppdaterat

2021-10-15