Crystal centering using deep learning in X-ray crystallography
Schurmann, Jonathan ; Lindhe, Isaak LU ; Janneck, Jorn W. LU ; Lima, Gustavo LU
and Matej, Zdenek
LU
(2020)
53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
p.978-983
- Abstract
A key challenge in X-ray crystallography is to find a good point on the crystal on which to center the beam because the crystal takes radiation damage after a number of shots which significantly distort the measurements. Therefore, the beam needs to be aimed manually by an operator, which results in significant additional effort and time.This paper presents an approach toward automating the beam aiming using machine learning, training a neural network with labeled data, resulting in a more efficient system that does not rely on manual supervision to determine where to aim the beam. A range of different neural network architectures are evaluated based on the accuracy of their predictions.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3dd23818-0a1e-4949-9e9e-5a1dd39f9ae5
- author
- Schurmann, Jonathan
; Lindhe, Isaak
LU
; Janneck, Jorn W.
LU
; Lima, Gustavo
LU
and Matej, Zdenek
LU
- organization
- publishing date
- 2020-03-30
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- crytallography, deep learning, machine learning, X-ray
- host publication
- 2019 53rd Asilomar Conference on Signals, Systems, and Computers
- editor
- Matthews, Michael B.
- article number
- 9048793
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
- conference location
- Pacific Grove, United States
- conference dates
- 2019-11-03 - 2019-11-06
- external identifiers
-
- scopus:85083298235
- ISBN
- 9781728143019
- 9781728143002
- DOI
- 10.1109/IEEECONF44664.2019.9048793
- language
- English
- LU publication?
- yes
- id
- 3dd23818-0a1e-4949-9e9e-5a1dd39f9ae5
- date added to LUP
- 2020-05-11 16:56:12
- date last changed
- 2024-05-29 13:21:10
@inproceedings{3dd23818-0a1e-4949-9e9e-5a1dd39f9ae5,
abstract = {{<p>A key challenge in X-ray crystallography is to find a good point on the crystal on which to center the beam because the crystal takes radiation damage after a number of shots which significantly distort the measurements. Therefore, the beam needs to be aimed manually by an operator, which results in significant additional effort and time.This paper presents an approach toward automating the beam aiming using machine learning, training a neural network with labeled data, resulting in a more efficient system that does not rely on manual supervision to determine where to aim the beam. A range of different neural network architectures are evaluated based on the accuracy of their predictions.</p>}},
author = {{Schurmann, Jonathan and Lindhe, Isaak and Janneck, Jorn W. and Lima, Gustavo and Matej, Zdenek}},
booktitle = {{2019 53rd Asilomar Conference on Signals, Systems, and Computers}},
editor = {{Matthews, Michael B.}},
isbn = {{9781728143019}},
keywords = {{crytallography; deep learning; machine learning; X-ray}},
language = {{eng}},
month = {{03}},
pages = {{978--983}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Crystal centering using deep learning in X-ray crystallography}},
url = {{http://dx.doi.org/10.1109/IEEECONF44664.2019.9048793}},
doi = {{10.1109/IEEECONF44664.2019.9048793}},
year = {{2020}},
}