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.

虽然第一原理方法已成功应用于表征多主元素合金 (MPEA) 的各个特性，但高计算要求阻碍了它们在寻找竞争特性之间的最佳权衡方面的使用。在这项工作中，我们提出了一个框架，通过将先进的从头开始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，并辅以提供简单趋势分析的简单分析模型。我们通过将框架应用于耐火 MPEA 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来有效计算强化和延展性模型的参数，考虑有限温度效应，并主动-学习从头开始数据参数化的矩张量势。随后，借助数据验证的分析模型，并依赖于一些特定于元素的参数和描述粘合的通用函数，从头计算获得的属性可用于将所有相关材料属性的预测扩展到一大类耐火合金元素之间。我们的研究结果为了解耐火 MPEA 的传统强度与延展性困境提供了重要见解。所提出的框架是通用的，可以扩展到其他感兴趣的材料和特性，从而能够在整个组合空间上对帕累托最优 MPEA 进行预测和易于处理的高通量筛选。