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Computing Anharmonic Free Energies in Solids with Machine-Learning Interatomic Potentials.

Jing Ma1,2,3, Liying An1,2,3, Huan Ma1,2,3

  • 1State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, PR China.

The Journal of Physical Chemistry Letters
|May 30, 2026
PubMed
Summary
This summary is machine-generated.

Calculating anharmonic free energies is crucial for accurate phase diagrams. This study introduces an efficient machine learning workflow that captures these effects, improving predictions for materials like Fe-C alloys.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid State Physics

Background:

  • Accurate free energies are vital for phase stability and phase diagram construction.
  • Traditional methods like harmonic/quasiharmonic approximations are inaccurate at high temperatures.
  • Thermodynamic integration is computationally intensive.

Purpose of the Study:

  • To develop an efficient workflow for calculating anharmonic free energies.
  • To combine nonequilibrium methods with machine learning interatomic potentials.
  • To construct accurate temperature- and composition-dependent phase diagrams.

Main Methods:

  • Developed an efficient workflow combining nonequilibrium approaches and machine learning potentials.
  • Validated the workflow using fcc Ag, hcp Ru, and bcc Mo.
  • Applied the framework to the Fe-C system under syngas conditions.

Main Results:

  • The workflow accurately captures anharmonic contributions in tested materials.
  • Finite-temperature vibrational free energies qualitatively alter phase stability in Fe-C.
  • Anharmonic effects significantly modify the stability window of χ-Fe5C2 compared to harmonic predictions.

Conclusions:

  • The developed framework provides a robust method for anharmonic free energy calculations.
  • This approach enables accurate phase diagram prediction in solids.
  • The findings are consistent with experimental observations for the Fe-C system.