Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides

The efficient design and discovery of stable inorganic crystal structures is central to materials innovation. Here, we compare data-driven approaches for accelerated crystal structure prediction: substitution into known prototype structures, generative artificial intelligence (genAI) using denoising diffusion as implemented in Chemeleon, and an evolutionary global optimization search. Candidate structures are optimized using an ensemble of machine-learned interatomic potentials, providing both energy estimates and uncertainty quantification. Applied to a large set of known and hypothetical ternary metal chalcogenide compositions, including technologically relevant sulfides such as Na₂SiS₃, RbPS₃, and KMo₂S₄, our analysis reveals that the genAI approach not only matches but can surpass traditional methods in identifying diverse, low-energy structures. These findings highlight the promise of generative models for scalable structural exploration of inorganic materials space.