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Assessing battery kinetics with machine learning
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DOI：10.1016/j.matt.2021.07.003.png)
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Abstract
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The ionic diffusion characteristics of electrode materials critically influence the performance of batteries. Over the last decade, disordered rocksalt materials have emerged as promising next-gen battery cathodes. The higher degree of disorder in these materials results in increased computational complexity when assessing ionic diffusion profiles. Recently in Electrochimica Acta, Chang, Jorgenson and co- authors reported a machine-learning-accelerated method to rapidly evaluate local ionic diffusion barriers in electrode materials with high accuracy.
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On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors.png)
ELECTROCHIMICA ACTA
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ELECTROCHIMICA ACTAIF5.6
OAAIShareSaveShareSaveShareSaveUnlocking the Potential of Cation-Disordered Oxides for Rechargeable Lithium BatteriesSCIENCEOAAI
SCIENCEIF45.8
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