Data driven design of compositionally complex energy materials

Compositionally complex materials have emerged as new frontier for sustainable energy storage and conversion. There are many unique features of compositional complex energy materials (CCEMs), include but not limited to less dependency of critical elements, the potential for enhanced ionic conduction, and the capability to prevent chemo-mechanical degradation. However, the design space of CCEMs is intricate due to higher dimensionality of compositional space, as well as convoluted interplay between long range order and short-range structures. This review aims at providing a concise summation of research frontier in CCEMs, covering aspects from synthesis, manipulation of local structures and property control. Given the rapid advancement of the battery field in recent years, this review will particularly emphasize on battery related CCEMs as a key area of representation. Furthermore, the common challenges and benefits of CCEMs will also be extrapolated to other fields of energy storage and conversion. Among the various prospects for utilizing data science in studying CCEMs, this work will primarily concentrate on the physical interpretation based on massive data generated by high-throughput computation. However, it will also encompass the cutting-edge progress of machine learning algorithms and their potential applications in the study of CCEMs.