Kavli Affiliate: Wei Gao
| First 5 Authors: Fei Shuang, Yucheng Ji, Zixiong Wei, Chaofang Dong, Wei Gao
| Summary:
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.
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