A novel computational framework for establishment of atomic mobility database directly from composition profiles and its uncertainty quantification

In this work, a novel computational framework for establishment of atomic mobility database directly from the experimental composition profiles and its uncertainty quantification was developed by merging the Bayesian inference with the Markov chain Monte Carlo algorithm into the latest version of the Hit DIC software. By treating the simulation of composition profiles with the composition-dependent coefficients as the forward problem, the inverse coefficient problem that provides the potential way to compute the atomic mobilities directly from composition profiles can be postulated. The values and uncertainties of the atomic mobility parameters of interest were assessed by means of Bayesian inference, where the composition profiles were consumed directly. Benchmark tests that consider the number of diffusion couples and the noise levels were conducted. Practical application of the current framework in determination of atomic mobility descriptions of fcc Ni-Ta and Ni-Al-Ta alloys was performed. Further discussion about the results of the benchmark tests and practical study case indicated that the present computational framework together with numbers of composition profiles from the multiple diffusion couples can help to establish the high-quality atomic mobility database of the target multicomponent alloys.