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

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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 LaFeO 3 and La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 − δ . LaFeO 3 was used as a benchmark material, and the QHA captures the qualitative expansion trend but overestimates the volumetric thermal expansion coefficient (TEC), giving α V = 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 α V = 3.56 × 10 − 5 K − 1 , in close agreement with the experiment. For La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 − 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 α L 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 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.
Funding Information
  • U.S. Department of Energy (DE-AC36-08GO28308)
  • National Energy Research Scientific Computing Center (BES-ERCAP0037132)
  • Office of Science
  • SLAC National Accelerator Laboratory
  • Office of Critical Minerals and Energy Innovation
  • Alternative Fuels and Feedstocks Office (DE-LC-0000022)
  • Office of Science of the U.S. Department of Energy (DE-AC02-05CH11231)
  • Office of Science of the U.S. Department of Energy (DE-AC02-76SF00515)
  • Office of Basic Energy Sciences (DE-AC02-76SF00515)
  • Stanford Synchrotron Radiation Lightsource

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