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

Aug 4, 2026

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Melting properties are essential for designing novel materials, particularly high-performance, high-melting refractory systems, yet experimental access is extremely difficult because of extreme melting temperatures. Conventional DFT-based free-energy approaches provide accuracy but require costly thermodynamic integration with ab initio molecular dynamics, making high-throughput screening impractical. This work introduces an efficient DFT method using a tailored machine-learning potential to closely reproduce the ab initio phase space and replace thermodynamic integration with faster free-energy perturbation calculations. For TaVCrW, the method computes melting temperature, enthalpy and entropy of fusion, volume change, and solid/liquid heat capacities, showing reasonable agreement with CALPHAD extrapolations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Challenges in experimental melting-property measurement

Conventional DFT-based free-energy approach and its cost

Prior acceleration strategies for solid and liquid free energies

Proposed efficient DFT method with machine learning

arXiv :2408 .08654v1 [ cond-mat .mtrl-sci ] 16 Aug 2024 Accelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW Li-Fang Zhu* , 1, 2 Fritz K¨ormann , 1, 2, 3 Qing Chen ,4 Malin Selleby ,5 J¨org Neugebauer , 1 and Blazej Grabowski 2 1 Department for Computational Materials Design, Max-Planck-Institut fu¨r Eisenforschung GmbH, Max-Planck-str.1, 40237 Du¨sseldorf, Germany 2 Institute for Materials Science, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany 3 Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-Universit¨at Bochum, 44801, Germany 4 Thermo-Calc Software AB, R˚asundav¨agen 18, 16967 Solna, Sweden 5 Department of Materials Science and Engineering, KTH (Royal Institute of Technology), SE-100 44 Stockholm, Sweden (Dated: August 19, 2024) Melting properties are critical for designing novel materials, especially for discovering highperformance, 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. I. INTRODUCTION Discovering novel high entropy alloys (HEAs) with exceptional performance has ushered in a new era for materials design 1–3 . The melting temperature is a crucial parameter in the search for such materials. For instance, a correlation between a high melting point and elevated temperature strength has been identified in refractory complex concentrated alloys4 . Besides the melting temperature, other melting properties, such as enthalpy and entropy of fusion, and volume change at the melting point, are also crucial for constructing phase diagramsand developing novel materials. However, experimental measurements on these properties, even for unary refractory materials, face severe challenges due to high melting points, often resulting in very scattered experimental data, if available at all. Additionally, the vast compositional space of HEAs makes systematic experimental screening of promising candidates impractical. Several computational methods for melting point predictions have been developed using empirical potentials, machine learning potentials, and density functional theory (DFT)5–7 . Calculations on other melting properties, such as entropy and enthalpy of fusion, volume expansion from solid to liquid at the melting point, and thermodynamic properties of the liquid phase (especially the liquid heat capacity), are, however, limited. They require access to the free energy surface of both solid and liquid, including all relevant physical contributions, such as vibrational entropy, including the anharmonic contribution, and electronic entropy, including the electronvibration coupling. These physical contributions

FAQs

Why are melting properties hard to measure for high-entropy refractory alloys?

High melting temperatures make experiments extremely challenging, often producing scattered data or no available measurements.

What computational bottleneck limits conventional DFT melting-property calculations?

The conventional free-energy approach typically requires expensive thermodynamic integration using ab initio molecular dynamics.

How does the proposed machine-learning-aided method reduce computation time?

A specially designed machine-learning potential reproduces the ab initio phase space, enabling the costly thermodynamic integration to be replaced by more efficient free-energy perturbation calculations.

Aug 4, 2026

Upload by Ezra