paper

Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction

arXiv:1812.02708 · doi:10.1103/PhysRevMaterials.3.035404

Abstract

We report the computational investigation of a series of ternary X $_{4}$ Y $_{2}$ Z and X $_{5}$ Y $_{2}$ Z $_{2}$ compounds with X={Mg, Ca, Sr, Ba}, Y={P, As, Sb, Bi}, and Z={S, Se, Te}. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on $ab initio$ calculations, we predict that many of these compounds are thermodynamically stable. In particular, 21 of the X $_{4}$ Y $_{2}$ Z compounds crystallize in a tetragonal structure with $I-42d$ symmetry, and exhibit band gaps in the range of 0.3 and 1.8 eV, well suited for various energy applications. We show that several candidate compounds (in particular X $_{4}$ Y $_{2}$ Te and X $_{4}$ Sb $_{2}$ Se) exhibit good photo absorption in the visible range, while others (e.g., Ba $_{4}$ Sb $_{2}$ Se) show excellent thermoelectric performance due to a high power factor and extremely low lattice thermal conductivities.