Cargando…

Correcting systematic errors in diffraction data with modern scaling algorithms

X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors...

Descripción completa

Detalles Bibliográficos
Autores principales: Aldama, Luis A., Dalton, Kevin M., Hekstra, Doeke R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478637/
https://www.ncbi.nlm.nih.gov/pubmed/37584427
http://dx.doi.org/10.1107/S2059798323005776
_version_ 1785101397617803264
author Aldama, Luis A.
Dalton, Kevin M.
Hekstra, Doeke R.
author_facet Aldama, Luis A.
Dalton, Kevin M.
Hekstra, Doeke R.
author_sort Aldama, Luis A.
collection PubMed
description X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.
format Online
Article
Text
id pubmed-10478637
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher International Union of Crystallography
record_format MEDLINE/PubMed
spelling pubmed-104786372023-09-06 Correcting systematic errors in diffraction data with modern scaling algorithms Aldama, Luis A. Dalton, Kevin M. Hekstra, Doeke R. Acta Crystallogr D Struct Biol Ccp4 X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods. International Union of Crystallography 2023-08-16 /pmc/articles/PMC10478637/ /pubmed/37584427 http://dx.doi.org/10.1107/S2059798323005776 Text en © Luis A. Aldama et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Ccp4
Aldama, Luis A.
Dalton, Kevin M.
Hekstra, Doeke R.
Correcting systematic errors in diffraction data with modern scaling algorithms
title Correcting systematic errors in diffraction data with modern scaling algorithms
title_full Correcting systematic errors in diffraction data with modern scaling algorithms
title_fullStr Correcting systematic errors in diffraction data with modern scaling algorithms
title_full_unstemmed Correcting systematic errors in diffraction data with modern scaling algorithms
title_short Correcting systematic errors in diffraction data with modern scaling algorithms
title_sort correcting systematic errors in diffraction data with modern scaling algorithms
topic Ccp4
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478637/
https://www.ncbi.nlm.nih.gov/pubmed/37584427
http://dx.doi.org/10.1107/S2059798323005776
work_keys_str_mv AT aldamaluisa correctingsystematicerrorsindiffractiondatawithmodernscalingalgorithms
AT daltonkevinm correctingsystematicerrorsindiffractiondatawithmodernscalingalgorithms
AT hekstradoeker correctingsystematicerrorsindiffractiondatawithmodernscalingalgorithms