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Updated: Jan 11, 2026

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

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Published on: January 16, 2021

On artificial crystal structure generation for solving the phase problem with deep learning.

Džonatans Miks Melgalvis¹, Toms Rekis²

¹Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas iela 1, Riga LV1004, Latvia.

Acta Crystallographica. Section A, Foundations and Advances

|November 11, 2025

Summary

This summary is machine-generated.

Artificial crystal structures aid neural networks in solving the crystallographic phase problem. Retraining the PhAI network on new artificial data significantly improves its ability to analyze larger unit-cell structures.

Keywords:

artificial crystal structures deep learning phase problem

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Area of Science:

Crystallography
Materials Science
Artificial Intelligence

Background:

Solving the crystallographic phase problem is crucial for determining crystal structures.
Neural networks offer a promising approach for phase retrieval.
Generating realistic artificial crystal structures is essential for training these networks.

Purpose of the Study:

To present and discuss methods for generating artificial crystal structures for neural network training.
To evaluate the performance of the PhAI neural network on experimental data after retraining with novel artificial datasets.

Main Methods:

Structure generation involves sampling unit-cell parameters and atom placement.
Lattice basis vectors are generated from sampled unit-cell volumes.
Molecule-like fragments are generated using database data to guide atom placement, complementing random methods.

Main Results:

The PhAI neural network was benchmarked and retrained using various artificial datasets.
Retraining PhAI with a new type of artificial data demonstrated significant improvements.
The improved model showed enhanced generalization for solving the phase problem in larger unit-cell structures.

Conclusions:

Artificial data generation is a viable strategy for enhancing neural network performance in crystallography.
The developed methods enable scalable generation of crystal structures for training.
The study highlights the potential of AI in advancing crystallographic structure determination.