Accelerated Screening of Ternary Chalcogenides for Potential Photovoltaic Applications

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 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 that of 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.

Automated summary

Chalcogenides, particularly ternary chalcogenides, have potential applications in optoelectronics, thermoelectrics, transparent contacts, and thin-film transistors. Despite the relatively low number of successfully synthesized compounds, there are numerous unexplored chalcogenides with fascinating physical characteristics. In this study, a systematic high-throughput screening process combined with first-principles calculations was implemented to study ternary chalcogenides and generate a large computational material database. Machine learning models were used to predict the thermodynamic stability of novel materials, and the electronic structures and performance of stable candidates were evaluated.