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

## TL;DR

This study combines machine learning and crystal structure prediction to identify stable ternary mixed-anion semiconductors with tunable band gaps suitable for energy applications.

## Contribution

It introduces a computational approach integrating machine learning and crystal structure prediction to discover new stable ternary semiconductors with adjustable electronic properties.

## Key findings

- 21 compounds with tetragonal structure and band gaps of 0.3-1.8 eV
- Several compounds show good visible light absorption
- Some compounds exhibit excellent thermoelectric performance

## 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 $\textit{ab initio}$ 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 $\textit{I-42d}$ 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.