Data-driven inverse design of MoNbTiVWZr refractory multicomponent alloys: Microstructure and mechanical properties

Multicomponent refractory alloys have the potential to operate in high-temperature environments. Alloys with heterogeneous/composite microstructure exhibit an optimal combination of high strength and ductility. The present work generates designed compositions using high-throughput computational and machine-learning (ML) models based on elements Mo-Nb-Ti-V-W-Zr manufactured utilizing vacuum arc melting. The experimentally observed phases were consistent with CALPHAD and Scheil simulations. ML models were used to predict the room temperature mechanical properties of the alloy and were validated with experimental mechanical data obtained from the three-point bending and compression tests. This work collectively showcases a data-driven, inverse design methodology that can effectively identify new promising multicomponent refractory alloys.