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.
Highlights
Interdiffusion involves in a variety of materials processes in metallic solids, for instance, solidification[1], solid solution[2], aging[3], corrosion[4], mutual interaction between coatings and matrix[5], and so on
HEAs, where the multiple components are presented as principal constituents, nowadays serve as the alternatives for many traditional alloy systems, where only one or two components are presented as principal constituents
An interesting hotspot arises from the intriguing interactions, where diffusion rates seem to be rather low among the composition space around the equal atomic composition space
Summary
Interdiffusion involves in a variety of materials processes in metallic solids, for instance, solidification[1], solid solution[2], aging[3], corrosion[4], mutual interaction between coatings and matrix[5], and so on. We are going to report a successful demonstration of an defined toolsets for numerical inverse methods are not yet automation computation framework for interdiffusivity evaluation enough to cope with dataset of large size for the complex and atomic mobility database development From the abroad comparison between the fitted and the model-predicted composition profiles, the generality ability of the selected parameters and evaluated uncertainties are reasonable. The variable-selection genetic algorithm performs slightly better, as the prediction biases tend to concentrate more obvious around zero than the others When it comes to the estimations produced by MCMC, the related fitting goodness is much better than the above three strategies as the related RSS is smaller. The evaluated effective tracer diffusion coefficients are qualitatively compared to the ones measured by Tsai et al.[6] and Vaidya et al.[9,49], as illustrated in
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
