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Machine learning reveals anharmonicity causing non-Arrhenius diffusion in tungsten

Published on:

1 November 2023

Primary Category:

Materials Science

Paper Authors:

Xi Zhang,

Sergiy V. Divinski,

Blazej Grabowski

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Key Details

Proposes efficient computational method to calculate diffusion coefficients

Applies this method to study self-diffusion in tungsten

Finds strong anharmonicity in vacancy formation and migration

This anharmonicity explains non-Arrhenius diffusion, not di-vacancies

Method enables accurate ab initio diffusion databases

AI generated summary

Machine learning reveals anharmonicity causing non-Arrhenius diffusion in tungsten

This paper proposes a new computational method to accurately calculate diffusion coefficients, even at high temperatures. Using this on tungsten, it finds strong anharmonic atomic vibrations that explain the experimentally observed non-Arrhenius diffusion behavior, contradicting the long-held assumption it was caused by di-vacancies.

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