Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912371783499776 |
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| author | Shuang, Fei Ji, Yucheng Wei, Zixiong Dong, Chaofang Gao, Wei Laurenti, Luca Dey, Poulumi |
| author_facet | Shuang, Fei Ji, Yucheng Wei, Zixiong Dong, Chaofang Gao, Wei Laurenti, Luca Dey, Poulumi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning Shuang, Fei Ji, Yucheng Wei, Zixiong Dong, Chaofang Gao, Wei Laurenti, Luca Dey, Poulumi Materials Science 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. |
| title | Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2409.14573 |