Accelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW

TL;DR

This paper introduces a machine learning-accelerated DFT method to efficiently predict melting properties of high-entropy alloys, significantly reducing computational costs while maintaining accuracy, demonstrated on the TaVCrW alloy.

Contribution

The authors develop a machine learning-enhanced free energy calculation method that reduces computational costs by 80% for ab initio melting property predictions of complex alloys.

Findings

Achieved 80% reduction in computational resources.

Predicted melting temperature, entropy, and enthalpy of fusion for TaVCrW.

Results align well with calphad extrapolated values.

Abstract

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. The conventional free energy approach using density functional theory (DFT) has been a gold standard for such purposes because of its high accuracy. However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase space, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the calphad extrapolated values.