Stability and Symmetry-Assured Crystal Structure Generation for Inverse Design of Photocatalysts in Water Splitting
Zhilong Song, Chongyi Ling, Qiang Li, Qionghua Zhou, and Jinlan Wang

TL;DR
This paper introduces SSAGEN, a novel generative framework that produces stable, symmetric crystal structures for photocatalysts, significantly improving stability metrics and ensuring target properties without fine-tuning.
Contribution
SSAGEN decouples structure generation into two stages, enhancing stability and symmetry in inverse design of materials, especially for photocatalytic water splitting.
Findings
Improves thermodynamic stability by 148% and kinetic stability by 180% over prior models.
Generates 200,000 structures with 81.2% novelty, 3,318 meeting all criteria.
DFT validation confirms 95.6% of generated structures meet PWS requirements.
Abstract
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Photocatalysis Techniques · TiO2 Photocatalysis and Solar Cells
