Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Más filtros

Bases de datos
Tipo del documento
País de afiliación
Intervalo de año de publicación
1.
Acta Crystallogr D Struct Biol ; 80(Pt 1): 26-43, 2024 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-38164955

RESUMEN

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.


Asunto(s)
Inteligencia Artificial , Sincrotrones , Cristalografía por Rayos X , Algoritmos , Simulación por Computador
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA