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Finite-Temperature Machine-Learning Molecular Dynamics for Thermal Expansion and Structural Evolution in Perovskite Oxide Electrode Materials

Igor Evangelista1,*, Mathias A. Kiefer2, Liam A. V. Nagle-Cocco2, Michael J. Dzara3, Nicholas A. Strange2, Michal Bajdich1, and Kirsten T. Winther1,†

  • *Contact author: igord@stanford.edu
  • †Contact author: winther@slac.stanford.edu
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PRX Energy 5, 043005 – Published 27 October, 2026

DOI: https://doi.org/10.1103/xwd4-x84g

Abstract

Thermal-expansion mismatch remains a major challenge for the long-term stability of solid oxide fuel-cell and electrolysis-cell materials, particularly for chemically complex perovskite electrodes operating at elevated temperatures. Reliable prediction of thermal expansion in these materials is difficult because anharmonic lattice dynamics, octahedral rotations, chemical disorder, and temperature-driven structural evolution can limit the applicability of conventional phonon-based approaches. Here, we combine quasiharmonic phonon calculations (QHA) and on-the-fly machine-learning force-field molecular dynamics (MLFF-MD) to evaluate finite-temperature structural behavior in LaFeO3 and La0.6Sr0.4Co0.2Fe0.8O3−δ. LaFeO3 was used as a benchmark material, and the QHA captures the qualitative expansion trend but overestimates the volumetric thermal expansion coefficient (TEC), giving αV=4.08×10−5  K−1 compared with the high-temperature x-ray diffraction (HT-XRD) value of 3.52×10−5  K−1. In contrast, MLFF-MD gives αV=3.56×10−5  K−1, in close agreement with the experiment. For La0.6Sr0.4Co0.2Fe0.8O3−δ, QHA becomes unreliable due to phonon instabilities, while MLFF-MD provides stable finite-temperature trajectories and captures the experimentally observed increase in linear TEC across the rhombohedral-to-cubic transition. Specifically, MLFF-MD predicts an increase in αL from 1.31×10−5  K−1 in the low-temperature rhombohedral regime to 1.90×10−5  K−1 in the high-temperature cubic regime, consistent with the structural evolution observed experimentally. The remaining quantitative discrepancy with HT-XRD is consistent with the stoichiometric model used here, which isolates the intrinsic thermal expansion while excluding chemical-expansion contributions associated with oxygen nonstoichiometry under experimental conditions. Once trained, the MLFF reduces the cost of an individual molecular-dynamics step by roughly 2 orders of magnitude relative to direct Ab Initio molecular dynamics, enabling substantially longer trajectories and broader sampling of thermally accessible configurations while retaining close agreement with DFT reference energies and forces. These results show that on-the-fly MLFF-MD provides a practical first-principles framework for predicting thermal expansion and phase evolution in disordered solid-oxide perovskites beyond the regime where phonon-based QHA remains reliable.

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Popular Summary

Solid oxide electrolysis cells and fuel cells can operate at high temperatures, where their components expand by different amounts. This mismatch can cause mechanical damage and shorten device lifetime. Here, the authors used machine-learning simulations and x-ray measurements to study thermal expansion in two oxide materials. The simulations draw on quantum-mechanical calculations but require much less computing time. They reproduce the measured expansion trends and describe how heating changes the atomic structure of one material, making it more symmetric on average. Conventional calculations have difficulty describing this behavior. The results suggest that this approach could help researchers predict thermal expansion and choose materials that remain compatible as a device heats up.

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PRX Energy 5, 043005– Published 27 October, 2026
  • Received 14 July 2026
  • Revised 11 September 2026
  • Accepted 25 September 2026
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