Abstract
Understanding the high-pressure behavior of sodium amide (NaNH2) is essential for its applications in hydrogen storage and chemical synthesis. Conventional structure prediction methods often struggle to 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. Here we introduce an experimental-informed deep learning generative framework for conditional crystal structure determination in NaNH2 under crystallographic constraints. The framework conditions on experimentally indexed lattice parameters and candidate space-group symmetry represents each structure using a direct-space asymmetric unit (DAU), and applies energy-guided diffusion sampling to generate low-enthalpy candidates. Applied to NaNH2, the workflow successfully identifies the high-pressure γ-phase as a P21/c structure (Z = 16, 64 atoms), which is validated by synchrotron X-ray diffraction and remains stable up to 14.0 GPa. Charge-density analysis and atomic rearrangement under compression elucidate the mechanisms driving the phase transitions and rationalize the stability of the γ-phase under high pressure. Overall, this work demonstrates a general strategy for resolving experimentally observed but structurally unsolved phases in complex ionic materials when lattice metrics and symmetry constraints are available.
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The data supporting the findings of this study are available within the Article and Supplementary Information; additional data are available from the corresponding author upon reasonable request.
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Acknowledgements
This work was supported by a Discovery Grant from the Natural Science and Engineering Research Council of Canada, and Defense Research and Development Canada under contract No. W7702-216248. The high-pressure experiments were partially performed at HPCAT (Sector 16), Advanced Photon Source (APS), Argonne National Laboratory. HPCAT operations are supported by the DOE-NNSA’s Office of Experimental Sciences. The Advanced Photon Source is a U.S. DOE Office of Science User Facility operated for the DOE by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The calculations were partially supported by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca) and Compute/Calcul Canada. R.G. thanks Dr. D. Zhao for helpful discussions and suggestions.
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Y.S. designed and supervised the project. A.L. performed the high-pressure experiments. R. G. performed the calculations and analyzed the results. R.G. and Y.S. wrote the manuscript.
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Guan, R., Liu, A. & Song, Y. Deep learning generative model for conditional crystal structure prediction of sodium amide. npj Comput Mater (2026). https://doi.org/10.1038/s41524-026-01994-2
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DOI: https://doi.org/10.1038/s41524-026-01994-2
