Machine learning in energy storage materials

With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution materials research paradigm. Here, taking dielectric capacitors and lithium-ion batteries as two representative examples, we review substantial advances development energy storage materials. First, a thorough discussion framework science is presented. Then, summarize applications from three aspects, including discovering designing novel materials, enriching theoretical simulations, assisting experimentation characterization. Finally, brief outlook highlighted to spark more insights on innovative implementation science.