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

doi:10.1021/acs.chemrev.1c00108

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

Université de Picardie Jules Verne
ALISTORE-ERI European Research Institute
Research center on batteries and supercapacitors

Research output: Contribution to journal › Review › peer-review

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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.

Original language	English
Journal	Chemical Reviews
Volume	122
Pages (from-to)	10899–10969
Number of pages	71
ISSN	0009-2665
DOIs	https://doi.org/10.1021/acs.chemrev.1c00108
Publication status	Published - 2022

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Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo-Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., Reynaud, M., Carrasco, J., Grimaud, A., Zhang, C., Vegge, T., Johansson, P., & Franco, A. A. (2022). Artificial Intelligence Applied to Battery Research: Hype or Reality? Chemical Reviews, 122, 10899–10969. https://doi.org/10.1021/acs.chemrev.1c00108

@article{89414e7783964373916603265c9e5173,

title = "Artificial Intelligence Applied to Battery Research: Hype or Reality?",

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.",

author = "Teo Lombardo and Marc Duquesnoy and Hassna El-Bouysidy and Fabian {\AA}r{\'e}n and Alfonso Gallo-Bueno and J{\o}rgensen, \{Peter Bj{\o}rn\} and Arghya Bhowmik and Arnaud Demorti{\`e}re and Elixabete Ayerbe and Francisco Alcaide and Marine Reynaud and Javier Carrasco and Alexis Grimaud and Chao Zhang and Tejs Vegge and Patrik Johansson and Franco, \{Alejandro A.\}",

year = "2022",

doi = "10.1021/acs.chemrev.1c00108",

language = "English",

volume = "122",

pages = "10899{\textendash}10969",

journal = "Chemical Reviews",

issn = "0009-2665",

publisher = "American Chemical Society",

}

Lombardo, T, Duquesnoy, M, El-Bouysidy, H, Årén, F, Gallo-Bueno, A, Jørgensen, PB, Bhowmik, A, Demortière, A, Ayerbe, E, Alcaide, F, Reynaud, M, Carrasco, J, Grimaud, A, Zhang, C, Vegge, T, Johansson, P & Franco, AA 2022, 'Artificial Intelligence Applied to Battery Research: Hype or Reality?', Chemical Reviews, vol. 122, pp. 10899–10969. https://doi.org/10.1021/acs.chemrev.1c00108

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 -

Artificial Intelligence Applied to Battery Research: Hype or Reality?

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