Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements
HJ Sun and C Zhang and WY Xia and L Tang and RH Wang and G Akopov and NW Hewage and KM Ho and K Kovnir and CZ Wang, INORGANIC CHEMISTRY, 61, 16699-16706 (2022).
We integrate a deep machine learning (ML) method with first principles calculations to efficiently search for the energetically favorable ternary compounds. Using La-Si-P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La- Si-P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La-Si-P structures from our ML-guided predictions, a number of low- energy La-X-P phases (X = Ge, Sn, Pb) are predicted.
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