Pan, T.Jin, S.Miller, M. D.Kyrillidis, A.Phillips, G. N.2023-08-012023-08-012023Pan, T., Jin, S., Miller, M. D., et al.. "A deep learning solution for crystallographic structure determination." <i>IUCrJ,</i> 10, no. 4 (2023) International Union of Crystallography: 487-496. https://doi.org/10.1107/S2052252523004293.https://hdl.handle.net/1911/115045The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.engExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.Journal articlemf5063https://doi.org/10.1107/S2052252523004293