Work

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

Year: 2025

Type: article

Abstract: We discuss and present approaches for generating artificial crystal structures for training neural networks to solve the phase problem. Structure generation is considered as a two-step process involving sampling unit-cell parameters and filling the unit cell with atoms. The former step includes generating lattice basis vectors from randomly sampled unit-cell volume. Apart from randomly placing atoms, we use database data to guide fast and scalable generation of molecule-like fragments. The recently developed neural network PhAI is then used as a benchmark and retrained with various sets of training data to assess how the corresponding models perform on experimental crystal structure data. We found a significant improvement in PhAI retrained on a new kind of artificial data to generalize the phase problem solution for larger unit-cell structures.

Source: Acta Crystallographica Section A Foundations and Advances

DOI: 10.1107/s2053273325009428

Authors: Džonatans Miks Melgalvis, Toms Rekis

Institutions: University of Latvia, Goethe University Frankfurt

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,430

Funders: Izglītības un zinātnes ministrija, Latvijas Zinātnes Padome, Latvijas Universitate, Latvijas Universitātes Fonds