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<p>Foundational machine learning potentials can alleviate the accuracy and transferability limitations of classical force fields. They can substantially expedite material design and discovery by providing microscopic insights into material behavior through Molecular Dynamics simulations. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. These models often exhibit significant deviations from experimentally observed phase transition temperatures by several hundred kelvins. Finetuning is therefore necessary to achieve adequate accuracy in many practical problems. This work proposes a top-down finetuning strategy that corrects inaccurately predicted transition temperatures using experimental reference data. We introduce the Differentiable Transition Temperature Correction method to minimize the free energy differences between phases at the experimental target pressures and temperatures. Using a benchmark of pure Titanium at pressures up to 5 GPa, we show that our approach substantially improves the predicted phase diagram and liquid-state diffusion constant. Transition temperatures are within tens of kelvins of the experimental reference. Our model-agnostic approach can be supplemented with top-down training on additional experimental properties. In principle, it is also generally applicable to correct other free energy differences beyond the field of materials science. Thus, our approach can serve as an essential step towards highly accurate machine learning potentials.</p>

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Refining machine learning potentials through thermodynamic theory of phase transitions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Paul Fuchs,
  • Julija Zavadlav

摘要

Foundational machine learning potentials can alleviate the accuracy and transferability limitations of classical force fields. They can substantially expedite material design and discovery by providing microscopic insights into material behavior through Molecular Dynamics simulations. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. These models often exhibit significant deviations from experimentally observed phase transition temperatures by several hundred kelvins. Finetuning is therefore necessary to achieve adequate accuracy in many practical problems. This work proposes a top-down finetuning strategy that corrects inaccurately predicted transition temperatures using experimental reference data. We introduce the Differentiable Transition Temperature Correction method to minimize the free energy differences between phases at the experimental target pressures and temperatures. Using a benchmark of pure Titanium at pressures up to 5 GPa, we show that our approach substantially improves the predicted phase diagram and liquid-state diffusion constant. Transition temperatures are within tens of kelvins of the experimental reference. Our model-agnostic approach can be supplemented with top-down training on additional experimental properties. In principle, it is also generally applicable to correct other free energy differences beyond the field of materials science. Thus, our approach can serve as an essential step towards highly accurate machine learning potentials.