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Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange-correlation
Pandu Wisesa1,2, Christopher M Andolina1, Wissam A Saidi1
1Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.
The Journal of Chemical Physics
|July 27, 2026
Summary
Predicting material melting points is crucial. This study found PBEsol generally offers better accuracy than PBE for elemental metals, though other factors also influence results.
Area of Science:
- Computational Materials Science
- Physical Chemistry
- Condensed Matter Physics
Background:
- Accurate melting temperature prediction is vital for materials design and high-temperature applications.
- The performance of density functional theory (DFT) functionals like PBE and PBEsol can vary for different material systems.
Purpose of the Study:
- To systematically assess the accuracy of PBE and PBEsol functionals in predicting melting temperatures of elemental metals.
- To investigate the influence of cohesive energy, liquid-phase energetics, and anharmonicity on melting point predictions.
Main Methods:
- Utilized the two-phase coexistence (TPC) approach combined with machine-learned interatomic potentials (MTP) for large-scale simulations.
- Simulated melting behavior for a curated set of elemental metals to minimize finite-size effects and ensure equilibration.
Main Results:
- TPC-MTP simulations revealed a clear dependence of predicted melting temperatures on the chosen DFT functional.
- PBEsol demonstrated superior overall agreement across the dataset compared to PBE, which performed well for lighter elements.
- Element-resolved trends were influenced by factors beyond cohesive energy, including liquid-phase properties and anharmonicity.
Conclusions:
- PBEsol is a more reliable choice for predicting melting points of elemental metals within the TPC-MTP framework.
- Accurate melting point simulations require careful selection of exchange-correlation functionals and consideration of multiple physical factors.
- The study highlights limitations in current DFT functionals for complex systems and emphasizes the need for advanced simulation methodologies.
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