Towards end-to-end structure determination from x-ray diffraction data using deep learning

G Guo, J Goldfeder, L Lan, A Ray, AH Yang… - npj Computational …, 2024 - nature.com
Powder crystallography is the experimental science of determining the structure of
molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) …

Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning

G Guo, J Goldfeder, L Lan, A Ray, AH Yang… - arXiv preprint arXiv …, 2023 - arxiv.org
The revolution in materials in the past century was built on a knowledge of the atomic
arrangements and the structure-property relationship. The sine qua non for obtaining …

CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention

C Dun, Q Pan, S Jin, R Stevens, MD Miller… - arXiv preprint arXiv …, 2023 - arxiv.org
Determining the structure of a protein has been a decades-long open question. A protein's
three-dimensional structure often poses nontrivial computation costs, when classical …

[HTML][HTML] CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention

T Pan, C Dun, S Jin, MD Miller, A Kyrillidis… - Structural …, 2024 - pubs.aip.org
Determining the atomic-level structure of a protein has been a decades-long challenge.
However, recent advances in transformers and related neural network architectures have …

Ligand identification in CryoEM and X-ray maps using deep learning

J Karolczak, A Przybyłowska, K Szewczyk… - …, 2025 - academic.oup.com
Motivation Accurately identifying ligands plays a crucial role in the process of structure-
guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample …

[HTML][HTML] Genetic Algorithm-Enhanced Direct Method in Protein Crystallography

R Fu, WP Su, H He - Molecules, 2025 - mdpi.com
Direct methods based on iterative projection algorithms can determine protein crystal
structures directly from X-ray diffraction data without prior structural information. However …

Diffusion Models Are Promising for Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data

G Guo, T Saidi, M Terban, SJL Billinge… - arXiv preprint arXiv …, 2024 - arxiv.org
A major challenge in materials science is the determination of the structure of nanometer
sized objects. Here we present a novel approach that uses a generative machine learning …

[PDF][PDF] Unravelling the components of diffuse scattering using deep learning

CA Fuller, LSP Rudden - IUCrJ, 2024 - journals.iucr.org
Many technologically important material properties are underpinned by disorder and short-
range structural correlations; therefore, elucidating structure–property relationships in …

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns

S Zhang, B Cao, T Su, Y Wu, Z Feng, J Xiong… - IUCrJ, 2024 - pmc.ncbi.nlm.nih.gov
Spectroscopic data, particularly diffraction data, are essential for materials characterization
due to their comprehensive crystallographic information. The current crystallographic phase …

Deep-learning map segmentation for protein X-ray crystallographic structure determination

P Skubák - Biological Crystallography, 2024 - journals.iucr.org
When solving a structure of a protein from single-wavelength anomalous diffraction X-ray
data, the initial phases obtained by phasing from an anomalously scattering substructure …