Machine Learning Design of Perovskite Catalytic Properties

Jacobs, Ryan; Liu, Jian; Abernathy, Harry; Morgan, Dane

doi:10.1002/aenm.202303684

Title: Machine Learning Design of Perovskite Catalytic Properties

Journal Article · 05 February 2024 · Advanced Energy Materials

DOI: https://doi.org/10.1002/aenm.202303684 · OSTI ID:2290405

^[1]; Liu, Jian ^[2]; Abernathy, Harry ^[2]; Morgan, Dane ^[1]

Department of Materials Science and Engineering University of Wisconsin‐Madison Madison WI 53706 USA
National Energy Technology Lab Morgantown WV 26505 USA

Abstract Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial‐to‐calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio‐derived features, potentially eliminating the need for ab initio calculations in descriptor‐based screening. The model of ASR enables temperature‐dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross‐validation reveals the model to be effective at discovering new promising materials prior to their initial discovery, demonstrating the model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, detailed discussion of different approaches of model featurization is provided for ML property prediction. Finally, the model is used to screen more than 19 million perovskites to develop a list of promising cheap, earth‐abundant, stable, and high performing materials, and find some top materials contain mixtures of less‐explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail.

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Sponsoring Organization:: USDOE

OSTI ID:: 2290405

Alternate ID(s):: OSTI ID: 2335311; OSTI ID: 2290407

Journal Information:: Advanced Energy Materials, Journal Name: Advanced Energy Materials Journal Issue: 12 Vol. 14; ISSN 1614-6832

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

Country of Publication:: Germany

Language:: English

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