Quantifying the contributions to diffusion in complex materials
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917654774677504 |
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| author | Chattopadhyay, Soham Trinkle, Dallas R. |
| author_facet | Chattopadhyay, Soham Trinkle, Dallas R. |
| contents | Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion--called "kinosons"--and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and elucidates kinetic mechanisms for diffusion. The distribution of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying the contributions to diffusion in complex materials Chattopadhyay, Soham Trinkle, Dallas R. Materials Science Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion--called "kinosons"--and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and elucidates kinetic mechanisms for diffusion. The distribution of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials. |
| title | Quantifying the contributions to diffusion in complex materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2401.06046 |