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扩散指的是粒子（即原子，分子或离子）的随机运动。单个粒子的运动是随机的，但它受系统的热力学和动力学状态控制。在复杂的系统中，不同类型的粒子间相互作用往往不同。随着体系中组元数目的增加，这种粒子相互作用会越来越复杂。以高熵合金为例，其复杂的粒子相互作用不但有助于高熵合金的缓慢扩散效应，而且还赋予了高熵合金优异的力学和服役性能。为了表征复杂的粒子相互作用对扩散过程的影响，人们希望用精准的数学物理函数来描述系统的扩散速率。多组（主）元合金系统所覆盖的成分范围广阔，而且扩散系数函数的参数评估需要以大量的实验数据为基础，因此建立精准的多元合金的扩散速率与成分以及温度的关系是一项极具挑战性的任务。
来自中南大学粉末冶金国家重点实验室的张利军教授团队基于数据挖掘技术，搭建了一个基于大规模数据集对扩散动力学数据库的参数进行自动化筛选、评估和不确定度量化的计算框架，并成功应用于CoCrFeMnNi高熵合金面心立方相扩散动力学数据库的建立。值得一提的是，该研究小组对CoCrFeMnNi高熵合金的扩散动力学数据库进行深入分析发现，高熵合金的扩散速率与其配置熵并无明显的相关性，而是呈现随成分、温度变化的复杂关系。他们的研究证明，自动化计算框架能够提供一种自适应和自更新的高质量数据库参数评估机制，展示了数据挖掘技术在扩散动力学数据库自动化计算和评估方面的巨大优势。自动化的扩散动力学数据库构建方法不仅可有效提高材料基因数据库的评估效率和质量，还能为揭示复杂的材料现象提供充分的数据支持。该文近期发表于npj Computational Materials 7: 35 (2021)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Automation of diffusion database development in multicomponent alloys from large number of experimental composition profiles
Jing Zhong, Li Chen & Lijun Zhang
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 expertise 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 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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