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<p>Negative Thermal Expansion (NTE) is a key property for achieving dimensional stability in advanced technologies, yet understanding its underlying mechanisms, particularly the dynamic processes of phase transitions, remains a formidable challenge. In this study, we applied large-scale molecular dynamics simulations using the Crystal Hamilton Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to elucidate the atomic-level mechanisms of the NTE phenomenon in the perovskite oxide system Bi<sub>1−<i>x</i></sub>La<sub><i>x</i></sub>CoO<sub>3</sub>. CHGNet achieved a computational speed approximately 20,000 times faster than first-principles calculations and qualitatively reproduced the chemical trend of the NTE transition temperature systematically decreasing as La substitution increased. Leveraging this computational efficiency, we successfully visualized the dynamic process of the NTE phase transition at the atomic level, which had previously been unobservable. The results revealed a nucleation and propagation mechanism wherein the phase transition initiates heterogeneously in La-rich regions and propagates to Bi-rich regions. These findings demonstrate that universal MLFFs are powerful tools for more quickly elucidating complex phase transition phenomena and open up new possibilities for computational science-driven exploration of new materials.</p>

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Large-scale molecular dynamics simulations of negative thermal expansion in Bi1−xLaxCoO3 using a pretrained machine learning force field

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Shogo Wakazaki,
  • Yuki Sakai,
  • Kazuki Takahashi,
  • Hena Das,
  • Masaki Azuma

摘要

Negative Thermal Expansion (NTE) is a key property for achieving dimensional stability in advanced technologies, yet understanding its underlying mechanisms, particularly the dynamic processes of phase transitions, remains a formidable challenge. In this study, we applied large-scale molecular dynamics simulations using the Crystal Hamilton Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to elucidate the atomic-level mechanisms of the NTE phenomenon in the perovskite oxide system Bi1−xLaxCoO3. CHGNet achieved a computational speed approximately 20,000 times faster than first-principles calculations and qualitatively reproduced the chemical trend of the NTE transition temperature systematically decreasing as La substitution increased. Leveraging this computational efficiency, we successfully visualized the dynamic process of the NTE phase transition at the atomic level, which had previously been unobservable. The results revealed a nucleation and propagation mechanism wherein the phase transition initiates heterogeneously in La-rich regions and propagates to Bi-rich regions. These findings demonstrate that universal MLFFs are powerful tools for more quickly elucidating complex phase transition phenomena and open up new possibilities for computational science-driven exploration of new materials.