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Article dans une revue

How Machine Learning Will Revolutionize Electrochemical Sciences

Abstract : Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.
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Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : mercredi 16 mars 2022 - 17:32:22
Dernière modification le : jeudi 29 septembre 2022 - 04:59:50

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Aashutosh Mistry, Alejandro A. Franco, Samuel J. Cooper, Scott A. Roberts, Venkatasubramanian Viswanathan. How Machine Learning Will Revolutionize Electrochemical Sciences. ACS Energy Letters, American Chemical Society 2021, 6 (4), pp.1422-1431. ⟨10.1021/acsenergylett.1c00194⟩. ⟨hal-03610987⟩



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