TGD-CSP: Reliable Crystal Structure Prediction with Template-Guided Diffusion and Energy-Based Optimization
Crystal Structure Prediction (CSP), the task of determining stable atomic arrangements from chemical composition alone, remains a central challenge in computational materials science with direct implications for accelerating materials discovery. While recent diffusion-based generative models achieve impressive results by conditioning on space-group symmetry information, this paradigm exposes three fundamental challenges: (1) unreliable symmetry inference from composition, (2) reliance on symmetry-only priors without comprehensive structural geometric guidance, and (3) prior-induced distribution shift caused by inaccurate or overly strong constraints. To address these challenges, we propose TGD-CSP, a three-stage generative framework that: (1) learns a cross-modal embedding space to retrieve structurally relevant templates directly from composition, thereby providing reliable symmetry priors; (2) guides diffusion-based generation via score-based conditioning that explicitly incorporates comprehensive geometric information from retrieved templates; and (3) fine-tunes the generative policy via reinforcement learning with an energy-based reward to alleviate prior-induced distribution shift and mitigate biased generation. % Evidence: Concrete results TGD-CSP achieves match rates of 74.56% and 75.25% on Perov-5 and MP-20, respectively, including a 42% relative improvement over state-of-the-art methods on Perov-5. It reduces RMSE to 0.0259 and 0.0258, respectively, while yielding structures with formation energies closely matching those of ground-truth structures. Our results demonstrate that TGD-CSP enables reliable generation from composition alone, significantly improving the practicality of generative models for real materials discovery.