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Applying machine learning to the design of materials for lithium ion battery

Wei Wu and Qiang Sun

Lithium ion batteries (LIBs) are widely used in electrical vehicle, portable electronic products and energy storage devices. It is in urgent need to develop novel battery materials with higher energy density, preeminent rate performance and better safety performance. Traditional experimental methods for developing new materials are time-consuming, while for conventional simulation methods a huge gap remains between computations and practical materials. In this regard, machine learning becomes promising due to its flexibility in dealing with complex problems. Since machine learning is a data driven method, no physical or chemical models are needed in the process. The results will be reliable as long as the data are consistent with the practical situation. An encouraging example is AlphaGo [1] that has exhibited the charm of machine learning.