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RIS citation export for FRBR06: Automated ML-based Sample Centering for Macromolecular X-Ray Crystallography with MXAimbot

AU  - Lindhé, I.
AU  - Aurelius, O.
AU  - Eguiraun, M.
AU  - Gonzalez, A.
AU  - Jagudin, E.
AU  - Janneck, J.W.
AU  - Lima, G.
AU  - Matej, Z.
AU  - Nan, J.
AU  - Schurmann, J.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Automated ML-based Sample Centering for Macromolecular X-Ray Crystallography with MXAimbot
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - MXAimbot is a neural network based tool, designed to automate the task of centering samples for macro-molecular X-ray crystallography experiments before exposing the sample to the beam. MXAimbot uses a convolutional neural network (CNN) trained on a few thousands images from an industrial vision camera pointed at the sample to predict suitable crystal centering for subsequent X-ray data collection. The motivation for this project is that the machine vision automated sample positioning allows X-ray laboratories and synchrotron beamlines to offer a more efficient alternative for the manual centering, which is time consuming and difficult to automate with conventional image analysis, and for the X-ray mesh scan centering, which can introduce radiation damage to the crystal. MXAimbot can be used to improve results of standard LUCID loop centering for fully automated data collection in fragment-screening campaigns. No need for sample rotation should be an additional advantage.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
ER  -