Determining crystal structures from powder X-ray diffraction (PXRD) has been a significant challenge in materials science, particularly when experimental data contain noise or the target structure has a high complexity. While recent AI generative models show promise for rapid structure generation, they predominantly employ data-driven approaches to learn direct mappings between PXRD patterns and crystal structures, often failing on complex or out-of-distribution cases. In this work, we present a hybrid ab-initio approach that decomposes structure determination into a two-stage optimization problem: (1) discrete selection of space group symmetry, unit cell parameters, and Wyckoff site combinations; and (2) continuous optimization of atomic coordinates within the selected Wyckoff positions. By integrating AI-based techniques for peak profile analysis, density estimation and energy minimization with physics-informed constraints, our method systematically overcomes limitations of purely data-driven PXRD solvers. We demonstrate that this hierarchical optimization framework enables robust structure determination even for challenging cases with high structural complexity or limited experimental data quality. Our approach provides a principled pathway for incorporating crystallographic knowledge into AI models for more reliable and generalizable crystal structure determination.
@article{arxiv.2605.24594,
title = {Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction},
author = {Kaixiang Su and Osman Goni Ridwan and Hongfei Xue and Qiang Zhu},
journal= {arXiv preprint arXiv:2605.24594},
year = {2026}
}