Machine learning-based strength prediction for refractory high-entropy alloys of the Al-Cr-Nb-Ti-V-Zr system

Abstract

The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling