Accelerated discovery of refractory high-entropy alloys for strength-ductility co-optimization: An exploration in NbTaZrHfMo system by machine learning

High temperature (HT) strength and room temperature (RT) ductility trade-offs are always unavoidable in the design of refractory high entropy alloys (RHEAs). Exploring the vast chemistry space to find optimally strong and ductile RHEAs remains a highly challenging task. Herein, we formulate a machine learning-based design strategy fusing uncertainty estimation and clustering analysis to explore a special alloy system (NbTaZrHfMo) for collaborative optimization of HT strength and RT ductility. Four non-equimolar alloys with a superior combination of HT strength, RT ductility and HT specific yield strength were discovered and synthetized. The influence of elements on mechanical properties are analyzed and an optimal composition range is identified based on model prediction. This work provides a general design approach enabling the concurrent optimization of conflicting properties with a small data-trained machine learning model, thereby producing a recipe to accelerate the discovery of desired RHEAs and other materials within a vast search space. In the present work, we formulate a data-driven strategy combining ML model prediction of alloy properties, EI-based uncertainty utility and clustering-based selection of alloy composition to systematically explore the NbTaZrHfMo chemical space to accelerate the design of RHEAs with a synergistic improvement of high temperature (HT) strength and room temperature (RT) ductility. We show how to accelerate the discovery of desired RHEAs in a vast chemistry space using small data-trained machine learning (ML) model. This approach is demonstrated as efficient, and four non-equimolar alloys with a superior combination of HT strength, RT ductility and HT specific yield strength are designed and synthetized, which are expected to serve as potential alternative materials for HT structural applications. This work provides a general design approach enabling the concurrent optimization of conflicting properties with a small data-trained machine learning model, thereby producing a recipe to accelerate the discovery of desired RHEAs and other materials systems within a vast search space. [Display omitted]