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

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.

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