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

Meena Rittiruam; Jakapob Noppakhun; Sorawee Setasuban; Nuttanon Aumnongpho; Attachai Sriwattana; Suphawich Boonchuay; Tinnakorn Saelee; Chanthip Wangphon; Annop Ektarawong; Patchanee Chammingkwan; Toshiaki Taniike; Supareak Praserthdam; Piyasan Praserthdam

doi:10.1038/s41598-022-21209-0

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

Sci Rep. 2022 Oct 5;12(1):16653. doi: 10.1038/s41598-022-21209-0.

Authors

Meena Rittiruam^{1

2

3}, Jakapob Noppakhun^{1

2

3}, Sorawee Setasuban^{1

2

4}, Nuttanon Aumnongpho^{1

2

3}, Attachai Sriwattana^{1

2

4}, Suphawich Boonchuay^{1

2

4}, Tinnakorn Saelee^{1

2

4}, Chanthip Wangphon^{1

2

4}, Annop Ektarawong^{5

6}, Patchanee Chammingkwan⁷, Toshiaki Taniike⁷, Supareak Praserthdam^{8

9}, Piyasan Praserthdam²

Affiliations

¹ High-Performance Computing Unit (CECC-HCU), Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Chulalongkorn University, Bangkok, 10330, Thailand.
² Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Chulalongkorn University, Bangkok, 10330, Thailand.
³ Rittiruam Research Group, Chulalongkorn University, Bangkok, 10330, Thailand.
⁴ Saelee Research Group, Chulalongkorn University, Bangkok, 10330, Thailand.
⁵ Extreme Conditions Physics Research Laboratory and Center of Excellence in Physics of Energy Materials (CE:PEM), Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
⁶ Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
⁷ Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan.
⁸ High-Performance Computing Unit (CECC-HCU), Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Chulalongkorn University, Bangkok, 10330, Thailand. supareak.p@chula.ac.th.
⁹ Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Chulalongkorn University, Bangkok, 10330, Thailand. supareak.p@chula.ac.th.

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 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.