标题： 高熵合金中固溶强化的精确从头算建模 显示英文标题

标题： Accurate ab initio modeling of solid solution strengthening in high entropy alloys

摘要： 高熵合金（HEA）代表一类具有前景特性的材料，如高强度和延展性、抗辐射损伤等。 同时，成分的组合多样性以及复杂的结构使得它们很难用传统方法进行研究。 在本工作中，我们提出了一种基于协同势近似内的从头算计算的计算效率高的方法。 为了使该方法具有预测性，我们对状态方程应用了交换-关联修正，并考虑了磁态和平衡体积的热效应。 该方法与现有固溶体整体性质的实验数据有良好的一致性。 作为具体案例，该工作流程应用于一系列铁族高熵合金，以在无需参数的模型基础上，研究其固溶强化。 结果揭示了合金组分之间的复杂相互作用，我们通过局部键合的简单模型对其进行分析。 由于计算效率高，该方法可以作为高熵合金最优设计的自适应学习工作流的基础。 显示英文摘要

摘要： High entropy alloys (HEA) represent a class of materials with promising properties, such as high strength and ductility, radiation damage tolerance, etc. At the same time, a combinatorially large variety of compositions and a complex structure render them quite hard to study using conventional methods. In this work, we present a computationally efficient methodology based on ab initio calculations within the coherent potential approximation. To make the methodology predictive, we apply an exchange-correlation correction to the equation of state and take into account thermal effects on the magnetic state and the equilibrium volume. The approach shows good agreement with available experimental data on bulk properties of solid solutions. As a particular case, the workflow is applied to a series of iron-group HEA to investigate their solid solution strengthening within a parameter-free model based on the effective medium representation of an alloy. The results reveal intricate interactions between alloy components, which we analyze by means of a simple model of local bonding. Thanks to its computational efficiency, the methodology can be used as a basis for an adaptive learning workflow for optimal design of HEA.