Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange-correlation functionals.
Accurate prediction of a material's melting temperature is critical for materials design and high-temperature applications. In this work, we investigate melting behavior across a deliberately selected set of elemental metals spanning systems where cohesive-energy trends suggest that PBE and PBEsol are expected to perform differently, as well as cases where their performance is ambiguous. Melting temperatures are computed using the two-phase coexistence (TPC) approach in conjunction with a machine-learned interatomic potential based on the moment tensor potential (MTP) framework, enabling large-scale simulations that minimize finite-size effects and ensure sufficient equilibration. The TPC-MTP results reveal a clear functional dependence in the predicted melting temperatures. PBE provides good agreement for several lighter elements, whereas PBEsol gives the best overall agreement across the full dataset. However, the element-resolved trends are not governed by cohesive energy alone, indicating that liquid-phase energetics, anharmonicity, and finite-temperature phase stability also contribute to the observed functional dependence. For intermediate and structurally complex systems, both functionals exhibit less systematic performance. Overall, this study provides a systematic assessment of functional-dependent melting temperature predictions, highlighting both the strengths and limitations of the combined TPC-MTP methodology and underscoring the need for carefully selected exchange-correlation treatments in high-accuracy melting-point simulations.