Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

Lithium batteries have revolutionized energy storage with their high energy density and long lifespan, but challenges such as energy density limitations, safety, and cost still need to be addressed. Crystalline materials, including Ni-rich cathodes and lithium anodes, play pivotal roles in the performance of high-energy-density lithium batteries. Understanding the micro-scale behavior and degradation mechanisms of these materials is crucial for improving macro-scale battery performance. Simulation methods, particularly machine learning (ML) techniques, have become indispensable tools in elucidating these intricate processes because of great efficiency and acceptable accuracy. ML methods depend on descriptors, which bridge the gap between crystal structures and input matrices of models. These descriptors encode essential atomic-level details in crystal structures, enabling predictions of material properties and behaviors relevant to lithium batteries. This paper reviews and discusses the diverse array of descriptors employed in the simulation of crystalline materials for lithium batteries with high energy density. Case studies highlight the effectiveness of different descriptors in simulating cathode behaviors such as Li/Ni disordering, screening of stable LiNi0.8Co0.1Mn0.1O2 (NMC811) configurations, and lithium deposition behaviors at the anode interface. The discussed descriptors can also be applied to other crystalline cathode, anode, and electrolyte materials in lithium batteries and advance the development of lithium batteries with superior energy density.