Computational Materials Design of High-Entropy Alloys Based on Full Potential Korringa-Kohn-Rostoker Coherent Potential Approximation and Machine Learning Techniques

Abstract

Computational materials design (CMD) based on the first-principles electronic structure calculations is demonstrated for two topics related to the design of high-entropy alloys (HEAs). The first one is a construction of prediction model of elastic constants. By applying machine learning technique with the use of the linearly independent descriptor generation method to the database of elastic constants of 2555 BCC HEAs generated by the full potential Korringa-Kohn-Rostoker coherent potential approximation (FPKKR-CPA) method. The obtained model is used to predict new HEAs with high Young's modulus. The second topic is a simulation of atomic arrangement in HEAs at finite temperature. In this simulation, HEAs are described by using the Potts-like model and the interaction parameters are determined based on the generalized perturbation method combined with the KKR-CPA method. Monte Carlo simulations for the models of CrMnFeCoNi and CrMnFeCoCu predict atomic arrangements which are consistent to the experimental observations.