High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

ABSTRACT: This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R2 closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 syst

SUBMITTER: Rittiruam M 

PROVIDER: S-EPMC9534987 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature