Research highlight: High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys – High-performance Computing Unit (CECC-HCU)

This work presented high-troughput materials screening using density functional theory (DFT) calculation and artificial neural network (ANN) for a lot of data of high-entropy alloys. The highlighted results are that

Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase
Hf, Ga, In, Sn, Pb, and Bi favor BCC phase
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
All predictions are in good agreement with the data from the literature.
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.

Read this research article: https://www.nature.com/articles/s41598-022-21209-0