Sampling-free computation of finite temperature material properties in isochoric and isobaric ensembles using the mean-field anharmonic bond model

Abstract

The recently introduced mean-field anharmonic bond model has shown remarkable accuracy in predicting finite temperature free energies for certain potential models of fcc crystals without thermodynamic sampling. In this work, we extend the model to treat modern machine learning potentials in both isochoric and isobaric ensembles while preserving existing vibrational correlations and ensuring thermodynamic self-consistency. Testing against molecular dynamics simulations of bulk fcc Al and Cu, we find free energies with an accuracy of a few meV/atom up to the melting temperature under typical operating pressures, with similar accuracy for thermal expansion. Our sampling-free estimation is universally superior to the quasiharmonic approximation for less than ten percent of the computational cost and many orders of magnitude more efficient than thermodynamic integration. We discuss applications of the method in modern computational materials science workflows. Published by the American Physical Society 2024