Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning

TL;DR

This study employs machine learning to uncover the super-Arrhenius hydrogen diffusion behavior in multi-principal element alloys, providing new analytical tools and insights for energy-related material design.

Contribution

It introduces a novel machine learning framework that accurately models hydrogen diffusion in MPEAs across compositional space and reveals the super-Arrhenius behavior with analytical expressions.

Findings

Hydrogen diffusion in MPEAs exhibits super-Arrhenius behavior.

The Vogel temperature correlates with the 5th percentile of H solution energy spectrum.

Chemical short-range order generally does not affect H diffusion, except with low concentrations of H-favoring elements.

Abstract

Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is essential for advancing clean energy technologies such as H transport, storage, and nuclear fusion applications. However, the vast compositional space and the intricate chemical environments inherent in MPEAs pose significant obstacles. In this work, we address this challenge by developing a multifaceted machine learning framework that integrates machine-learning force field, neural network-driven kinetic Monte Carlo, and machine-learning symbolic regression. This framework allows for accurate investigation of H diffusion across the entire compositional space of body-centered cubic (BCC) refractory MoNbTaW alloys, achieving density functional theory accuracy. For the first time, we discover that H diffusion in MPEAs exhibits super-Arrhenius behavior, described by the Vogel-Fulcher-Tammann model, where the Vogel temperature correlates with the 5th percentile of H solution energy spectrum. We also derive robust analytical expressions that can be used to predict H diffusivity in general BCC MPEAs. Our findings further elucidate that chemical short-range order (SRO) generally does not impact H diffusion, except it enhances diffusion when "H-favoring" elements (notably Nb and Ta) are present in low concentrations. These findings not only enhance our understanding of H diffusion dynamics in general MPEAs but also guide the development of advanced MPEAs in H-related applications by manipulating element type, composition and SRO.