  Chalcogenides, which refer to chalcogen anions, have attracted considerable
attention in multiple fields of applications, such as optoelectronics,
thermoelectrics, transparent contacts, and thin film transistors. In comparison
to oxide counterparts, chalcogenides have demonstrated higher mobility and
\textit{p}-type dopability, owing to larger orbital overlaps between metal-X
covalent chemical bondings and higher-energy valence bands derived by
p-orbitals. Despite the potential of chalcogenides, the number of successfully
synthesized compounds remains relatively low compared to oxides, suggesting the
presence of numerous unexplored chalcogenides with fascinating physical
characteristics. In this study, we implemented a systematic high-throughput
screening process combined with first-principles calculations on ternary
chalcogenides using 34 crystal structure prototypes. We generated a
computational material database containing over 400,000 compounds by exploiting
the ion-substitution approach at different atomic sites with elements in the
periodic table. The thermodynamic stabilities of the candidates were validated
using the chalcogenides included in the Open Quantum Materials Database.
Moreover, we trained a model based on Crystal Graph Convolutional Neural
Networks to predict the thermodynamic stability of novel materials.
Furthermore, we theoretically evaluated the electronic structures of the stable
candidates using accurate hybrid functionals. A series of in-depth
characteristics, including the carrier effective masses, electronic
configuration, and photovoltaic conversion efficiency, was also investigated.
Our work provides useful guidance for further experimental research in the
synthesis and characterization of such chalcogenides as promising candidates,
as well as charting the stability and optoelectronic performance of ternary
chalcogenides.
