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

Sendek, Austin D.; Ransom, Brandi; Cubuk, Ekin D.; Pellouchoud, Lenson A.; Nanda, Jagjit; Reed, Evan J.

doi:10.1002/aenm.202200553

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

Journal Article · 28 June 2022 · Advanced Energy Materials

DOI: https://doi.org/10.1002/aenm.202200553 · OSTI ID:1996458

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Aionics, Inc. 221 10 ^th Street Evanston WY 82930, USA, Department of Materials Science and Engineering Stanford University 496 Lomita Mall Stanford CA 94305, USA
Department of Materials Science and Engineering Stanford University 496 Lomita Mall Stanford CA 94305, USA
Google Research Brain Team 1600 Amphitheater Pkwy. Mountain View CA 94043, USA
Aionics, Inc. 221 10 ^th Street Evanston WY 82930, USA
Chemical Sciences Division Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge TN 37831, USA

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.

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Sponsoring Organization:: USDOE

OSTI ID:: 1996458

Journal Information:: Advanced Energy Materials, Journal Name: Advanced Energy Materials Journal Issue: 31 Vol. 12; ISSN 1614-6832

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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