Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

TL;DR

This paper introduces an ab initio machine-learning framework to accurately compute vacancy-mediated self-diffusion in tungsten, revealing strong anharmonic effects that explain its non-Arrhenius behavior and aligning well with experimental data.

Contribution

It develops a novel computational approach combining ab initio methods and machine learning to study diffusion, capturing anharmonic effects and extending applicability to complex alloys.

Findings

Strong anharmonicity influences tungsten self-diffusion.

Calculated diffusivity matches experimental temperature dependence.

Framework is applicable to complex high-entropy alloys.

Abstract

We propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at density-functional-theory level. With the aid of a bespoke machine-learning interatomic potential, the temperature-dependent vacancy formation and migration Gibbs energies of the prototype system body-centered cubic (BCC) tungsten are shown to be strongly affected by anharmonicity. This finding explains the physical origin of the experimentally observed non-Arrhenius behavior of tungsten self-diffusion. A remarkable agreement between the calculated and experimental temperature-dependent self-diffusivity and, in particular, its curvature is revealed. The proposed computational framework is robust and broadly applicable, as evidenced by the first tests for a hexagonal close-packed (HCP) multicomponent high-entropy alloy. The successful applications underscore the attainability of an accurate ab initio diffusion database.