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Abstract：
While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use in searching for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a framework to explore Pareto-optimal compositions by integrating advanced ab initio-based techniques into a Bayesian multi-objective optimization workflow, complemented by a simple analytical model providing straightforward analysis of trends. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Properties obtained from ab initio calculations are subsequently used to extend predictions of all relevant material properties to a large class of refractory alloys with the help of the analytical model validated by the data and relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.
摘要：
虽然第一性原理方法已被成功地应用于表征多主元素合金（MPEA）的各个特性，它们在寻找竞争属性之间的最佳权衡中的使用受到高计算要求的阻碍。在这项工作中,我们提出了一个框架，通过将先进的基于从头算的技术集成到贝叶斯多目标优化工作流程中来探索帕累托最优组合物，辅之以简单的分析模型，提供对趋势的直接分析。我们通过将其应用于耐火MPEAs的固溶强化和延展性来对框架进行基准测试，使用相干势近似方法的组合有效地计算了增强和延性模型的参数，考虑到有限的温度效应，和主动学习的矩张量势用从头算数据参数化。从头计算获得的特性随后被用于将所有相关材料特性的预测扩展到一大类耐火合金，并借助由数据验证的分析模型，并依赖于一些元素特定的参数和描述元素之间结合的通用函数。我们的发现为耐火MPEA的传统强度与延展性困境提供了至关重要的见解。所提出的框架是通用的，可以扩展到其他感兴趣的材料和属性，在整个组成空间中实现帕累托最优MPEA的预测性和易于处理的高通量筛选。
