High-Entropy Materials Design by Integrating the First-Principles Calculations and Machine Learning: a Case Study in the Al-Co-Cr-Fe-Ni System

Abstract The first-principles calculation is widely used in high-entropy materials. However, this approach may consume many computational resources for complex systems, limiting the development of property maps for the related materials across the whole composition range. In this work, the most prevalent Al-Co-Cr-Fe-Ni system (both FCC and BCC) is chosen for our investigation. A comprehensive database of properties (e.g., phase stabilities and elastic properties) was established by combining the first-principles calculation results and machine learning: starting from unary, binary, ternary, and quaternary, then extending into quinary systems. A comparable software program was also developed by utilizing this database. Furthermore, the information/mechanism that underlies the database was fully studied by screening and statistical analysis.