Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 29, 2022
- Source ID
- 10.1002/aenm.202200553
Entities
People
- Austin D Sendek
- Brandi Ransom
- Ekin Cubuk
- Evan J Reed
- Jagjit Nanda
- Lenson A. Pellouchoud
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- Oak Ridge National Laboratory
- Office of Energy Efficiency and Renewable Energy
- Stanford University
- United States Department of Energy
- Vehicle Technologies Office