期刊
CRYSTALS
卷 9, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/cryst9010054
关键词
battery materials; machine learning; materials discovery
资金
- NSF CAREER [1651668]
- Harvard Future Faculty Leaders Postdoctoral Fellowship
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1651668] Funding Source: National Science Foundation
Batteries are a critical component of modern society. The growing demand for new battery materialscoupled with a historically long materials development timehighlights 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.
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