Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime. Issue 31 (29th June 2022) - Sendek, Austin D.; Ransom, Brandi; Cubuk, Ekin D.; Pellouchoud, Lenson A.; Nanda, Jagjit; Reed, Evan J.
Abstract: Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints. Abstract : Machine learning (ML)‐based modeling can accelerate the design of battery materials, but it may be unclear in some cases whether ML can play a useful role. In this review, the fundamentals of ML modeling are presented and best practices are illustrated with examples from original and published data. The potential for ML modeling of key battery design areas is discussed.