Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

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.

Automated summary

This article discusses the application of machine learning-based battery modeling in the small data domain. Through the case study of ionic conductivity modeling, the working principle of ML battery modeling and methods for judging model performance are analyzed in depth. Recommendations for building models in the small data regime are provided, and promising future directions in battery design modeling are identified.