Artificial Intelligence Applied to Battery Research: Hype or Reality?
Reviewartikel, 2022

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

Centre national de la recherche scientifique (CNRS)

Marc Duquesnoy

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Hassna El-Bouysidy

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Fabian Årén

Chalmers, Fysik, Materialfysik

Centre national de la recherche scientifique (CNRS)

Alfonso Gallo-Bueno

Basque Research and Technology Alliance (BRTA)

Centre national de la recherche scientifique (CNRS)

Peter Bjørn Jørgensen

Centre national de la recherche scientifique (CNRS)

Danmarks Tekniske Universitet (DTU)

Arghya Bhowmik

Danmarks Tekniske Universitet (DTU)

Centre national de la recherche scientifique (CNRS)

Arnaud Demortière

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Elixabete Ayerbe

Centre national de la recherche scientifique (CNRS)

Centro de Investigacion Tecnológica En Electroquimica

Francisco Alcaide

Centre national de la recherche scientifique (CNRS)

Centro de Investigacion Tecnológica En Electroquimica

Marine Reynaud

Centre national de la recherche scientifique (CNRS)

Basque Research and Technology Alliance (BRTA)

Javier Carrasco

Centre national de la recherche scientifique (CNRS)

Basque Research and Technology Alliance (BRTA)

Alexis Grimaud

Centre national de la recherche scientifique (CNRS)

Sorbonne Université

Collège de France

Chao Zhang

Centre national de la recherche scientifique (CNRS)

Uppsala universitet

T. Vegge

Centre national de la recherche scientifique (CNRS)

Danmarks Tekniske Universitet (DTU)

Patrik Johansson

Centre national de la recherche scientifique (CNRS)

Chalmers, Fysik, Materialfysik

Alejandro A. Franco

Institut Universitaire de France

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Chemical Reviews

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

Vol. 122 12 10899 -10969

Högkoncentrerade elektrolyter

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

Tillämpad AI för batteri F&U

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

Styrkeområden

Transport

Energi

Materialvetenskap

Ämneskategorier

Design

Systemvetenskap

Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

DOI

10.1021/acs.chemrev.1c00108

PubMed

34529918

Mer information

Senast uppdaterat

2022-11-21