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

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 $\text{ab initio}$ 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 $\text{I-42d}$ 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.

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Cited by in corpus (1)

• Training machine-learning potentials for crystal structure prediction using disordered structures