Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys, a systematic assessment remains inaccessible via Edisonian approaches. We approach this challenge by considering the specific case of alloy hardness, and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space. The model, tested on diverse datasets, was used to explore and successfully predict hardness in Al_xTi_y(CrFeNi)_1-x-y, Hf_xCo_y(CrFeNi)_1-x-y and Al_x(TiZrHf)_1-x systems supported by data from density-functional theory predicted phase stability and ordering behavior. The experimental validation of hardness was done on TiZrHfAl_x. The selected systems pose diverse challenges due to the presence of ordering and clustering pairs, as well as vacancy-stabilized novel structures. We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process.

尽管产生了大量关于多主元素合金力学行为的数据，但通过爱迪生的方法仍然无法进行系统的评估。我们通过考虑合金硬度的具体情况来应对这一挑战，并提出了一个机器学习框架，该框架捕获了有助于硬度的基本物理特征，并允许对多维成分空间进行高通量探索。该模型在不同的数据集上进行了测试，用于探索和成功预测 Al _x Ti _y (CrFeNi) _{1- x - y}、Hf _x Co _y (CrFeNi) _{1- x - y}和 Al _x的硬度(TiZrHf)由密度泛函理论数据支持的_{1- x系统预测了相稳定性和有序行为。}硬度的实验验证是在 TiZrHfAl _x上完成的。由于存在排序和聚类对以及空位稳定的新结构，所选系统带来了各种挑战。我们还提出了一个详细的模型分析，该分析将局部部分依赖性与成分刺激和模型响应研究相结合，以从决策过程中获得特定于材料的见解。