Abstract We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties.

中文翻译：

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具有所需性能的高熵合金的机器学习辅助设计

摘要 我们制定了一种材料设计策略，将机器学习 (ML) 代理模型与实验设计算法相结合，以在模型 Al-Co-Cr-Cu-Fe-Ni 系统中搜索具有大硬度的高熵合金 (HEAs)。我们仅通过七次实验就制造了几种合金，其硬度比原始训练数据集中的最佳值高 10%。我们发现，基于对 HEA 属性的了解，同时使用组合物和描述符的策略优于仅基于组合物的策略。该策略提供了一种快速优化多组分系统的方法，例如块状金属玻璃和高温合金，以达到所需的性能。