publications

2024

1. 

   Deep residual networks for crystallography trained on synthetic data

   Derek Mendez, James M. Holton, Artem Y. Lyubimov, Sabine Hollatz, Irimpan I. Mathews, Aleksander Cichosz, Vardan Martirosyan, Teo Zeng, Ryan Stofer, Ruobin Liu, Jinhu Song, Scott McPhillips, Mike Soltis, and Aina E. Cohen

   Acta Crystallographica Section D, Jan 2024

   Abs HTML

   The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called \it Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of \it Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. \it Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While \it Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.