XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction

TL;DR

XDXD is a novel end-to-end deep learning framework that accurately determines complete atomic crystal structures directly from low-resolution X-ray diffraction data, overcoming interpretability challenges of traditional methods.

Contribution

It introduces the first diffusion-based generative model for direct atomic model prediction from low-resolution diffraction data, eliminating manual map interpretation.

Findings

Achieves 70.4% structure match rate at 2.0 Å resolution

Demonstrates robustness on 24,000 experimental structures

Shows potential for extending to complex systems like peptides

Abstract

Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~\AA{} resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases.