Deep residual networks for crystallography trained on synthetic data.
Acta Crystallogr D Struct Biol
; 80(Pt 1): 26-43, 2024 Jan 01.
Article
en En
| MEDLINE
| ID: mdl-38164955
ABSTRACT
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 Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. 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 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.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Sincrotrones
Idioma:
En
Revista:
Acta Crystallogr D Struct Biol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos