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Crystals 2019, 9(1), 54; https://doi.org/10.3390/cryst9010054

Data-Driven Studies of Li-Ion-Battery Materials

1
Materials Science & Engineering Department, University of Utah, Salt Lake City, UT 84112, USA
2
The Department of Physics, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 9 December 2018 / Revised: 13 January 2019 / Accepted: 14 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue New Materials for Li-Ion Batteries)
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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. View Full-Text
Keywords: battery materials; machine learning; materials discovery battery materials; machine learning; materials discovery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kauwe, S.K.; Rhone, T.D.; Sparks, T.D. Data-Driven Studies of Li-Ion-Battery Materials. Crystals 2019, 9, 54.

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