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• W2909102051 abstract "Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials." @default.
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• W2909102051 date "2019-01-18" @default.
• W2909102051 modified "2023-10-11" @default.
• W2909102051 title "Data-Driven Studies of Li-Ion-Battery Materials" @default.
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• W2909102051 doi "https://doi.org/10.3390/cryst9010054" @default.
• W2909102051 hasPublicationYear "2019" @default.
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