Automation of diffusion database development in multicomponent alloys from large number of experimental composition profiles
Abstract Nowadays, the urgency for the high-quality interdiffusion coefficients and atomic mobilities with quantified uncertainties in multicomponent/multi-principal element alloys, which are indispensable for comprehensive understanding of the diffusion-controlled processes during their preparation...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/339b9f813184422d8704b8ea08ec4a21 |
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| Sumario: | Abstract Nowadays, the urgency for the high-quality interdiffusion coefficients and atomic mobilities with quantified uncertainties in multicomponent/multi-principal element alloys, which are indispensable for comprehensive understanding of the diffusion-controlled processes during their preparation and service periods, is merging as a momentous trending in materials community. However, the traditional exploration approach for database development relies heavily on expertize and labor-intensive computation, and is thus intractable for complex systems. In this paper, we augmented the HitDIC (high-throughput determination of interdiffusion coefficients, https://hitdic.com ) software into a computation framework for automatic and efficient extraction of interdiffusion coefficients and development of atomic mobility database directly from large number of experimental composition profiles. Such an efficient framework proceeds in a workflow of automation concerning techniques of data-cleaning, feature engineering, regularization, uncertainty quantification and parallelism, for sake of agilely establishing high-quality kinetic database for target alloy. Demonstration of the developed infrastructures was finally conducted in fcc CoCrFeMnNi high-entropy alloys with a dataset of 170 diffusion couples and 34,000 composition points for verifying their reliability and efficiency. Thorough investigation over the obtained kinetic descriptions indicated that the sluggish diffusion is merely unilateral interpretation over specific composition and temperature ranges affiliated to limited dataset. It is inferred that data-mining over large number of experimental data with the combinatorial infrastructures are superior to reveal extremely complex composition- and temperature-dependent thermal–physical properties. |
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