Deep learning generative model for conditional crystal structure prediction of sodium amide

Understanding the high-pressure behavior of sodium amide (NaNH2) is essential for its applications in hydrogen storage and chemical synthesis. Conventional structure prediction methods have been shown to struggle in accurately capture its pressure-induced phase transitions due to the complexity of its potential energy surface, which arises from strong ionic interactions, large unit cell size, and significant atomic rearrangements under compression. In this study, we introduce a deep learning generative framework that enables conditional crystal structure prediction by incorporating lattice parameters and space group symmetry as explicit constraints, and applying energy-guided diffusion sampling during generation. The framework uses a direct-space asymmetric unit (DAU) to represent each structure, enabling symmetry-consistent encoding and efficient reconstruction of full crystals through Wyckoff operations. Applied to NaNH2, this method successfully predicts the high-pressure γ-phase as a P21/c structure (Z = 16, 64 atoms), which was previously missed by conventional approaches. This phase is confirmed by synchrotron X-ray diffraction and remains stable up to 14.0 GPa. Further analysis of charge density and atomic rearrangement reveals the mechanisms driving the phase transitions and explains the robust stability of the γ-phase under high pressure. This work demonstrates the potential of our deep learning generative framework to accelerate structure prediction in complex ionic materials and provides an effective framework for modeling crystals with large unit cells accurately.