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 HitDIC 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.

在这项工作中，通过将贝叶斯推论和马尔可夫链蒙特卡罗算法合并到最新版本的HitDIC软件中，开发了一种直接从实验组成轮廓及其不确定性定量建立原子迁移率数据库的新颖计算框架。通过将与组成相关的系数作为正向问题来处理组成轮廓的模拟，可以提出反系数问题，该系数提供了直接从组成轮廓中计算原子迁移率的潜在方法。感兴趣的原子迁移率参数的值和不确定性通过贝叶斯推断进行评估，其中成分分布被直接消耗掉。进行了考虑扩散对数和噪声水平的基准测试。进行了当前框架在确定fcc Ni-Ta和Ni-Al-Ta合金原子迁移率描述中的实际应用。对基准测试结果和实际研究案例的进一步讨论表明，当前的计算框架以及来自多个扩散对的成分分布数量可以帮助建立目标多组分合金的高质量原子迁移率数据库。