Stability and Symmetry-Assured Crystal Structure Generation for Inverse Design of Photocatalysts in Water Splitting

Generative models are revolutionizing materials discovery by enabling inverse design-direct generation of structures from desired properties. However, existing approaches often struggle to ensure inherent stability and symmetry while precisely generating structures with target compositions, space groups, and lattices without fine-tuning. Here, we present SSAGEN (Stability and Symmetry-Assured GENerative framework), which overcomes these limitations by decoupling structure generation into two distinct stages: crystal information (lattice, composition, and space group) generation and coordinate optimization. SSAGEN first generates diverse yet physically plausible crystal information, then derives stable and metastable atomic positions through universal machine learning potentials, combined global and local optimization with symmetry and Wyckoff position constraints, and dynamically refined search spaces. Compared to prior generative models such as CDVAE, SSAGEN improves the thermodynamic and kinetic stability of generated structures by 148% and 180%, respectively, while inherently satisfying target compositions, space groups, and lattices. Applied to photocatalytic water splitting (PWS), SSAGEN generates 200,000 structures-81.2% novel-with 3,318 meeting all stability and band gap criteria. Density functional theory (DFT) validation confirms 95.6% structures satisfy PWS requirements, with 24 optimal candidates identified through comprehensive screening based on electronic structure, thermodynamic, kinetic, and aqueous stability criteria. SSAGEN not only precisely generates materials with desired crystal information but also ensures inherent stability and symmetry, establishing a new paradigm for targeted inverse design of functional materials.