Year: 2026
Type: article
Abstract: Crystal structure determination from low resolution diffraction data is challenging due to lack of a general-purpose method for solving the crystallographic phase problem. We have developed graph neural network GraPhAI and propose a new and efficient diffraction data representation in a graph form for deep learning. The trained GraPhAI models are intended for ab initio phasing of down to 2 Å resolution data of typical unit cell volume centrosymmetric crystal structures. The success rate is above 80% for structures containing an atom with Z ≥ 19 (e.g. metal-organic frameworks, coordination compounds, or inorganic structures). For purely organic crystal structures, the success so far is limited.
DOI: 10.1021/jacs.6c05607
Authors: Džonatans Miks Melgalvis, Toms Rekis
Institutions: University of Latvia, Goethe University Frankfurt
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Award: G4781514827
OpenAlex ID: W7166700904