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Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensiona...

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Autores principales: Chitturi, Sathya R., Ratner, Daniel, Walroth, Richard C., Thampy, Vivek, Reed, Evan J., Dunne, Mike, Tassone, Christopher J., Stone, Kevin H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662964/
https://www.ncbi.nlm.nih.gov/pubmed/34963768
http://dx.doi.org/10.1107/S1600576721010840
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author Chitturi, Sathya R.
Ratner, Daniel
Walroth, Richard C.
Thampy, Vivek
Reed, Evan J.
Dunne, Mike
Tassone, Christopher J.
Stone, Kevin H.
author_facet Chitturi, Sathya R.
Ratner, Daniel
Walroth, Richard C.
Thampy, Vivek
Reed, Evan J.
Dunne, Mike
Tassone, Christopher J.
Stone, Kevin H.
author_sort Chitturi, Sathya R.
collection PubMed
description A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.
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spelling pubmed-86629642021-12-27 Automated prediction of lattice parameters from X-ray powder diffraction patterns Chitturi, Sathya R. Ratner, Daniel Walroth, Richard C. Thampy, Vivek Reed, Evan J. Dunne, Mike Tassone, Christopher J. Stone, Kevin H. J Appl Crystallogr Research Papers A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution. International Union of Crystallography 2021-11-30 /pmc/articles/PMC8662964/ /pubmed/34963768 http://dx.doi.org/10.1107/S1600576721010840 Text en © Sathya R. Chitturi et al. 2021 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 Research Papers
Chitturi, Sathya R.
Ratner, Daniel
Walroth, Richard C.
Thampy, Vivek
Reed, Evan J.
Dunne, Mike
Tassone, Christopher J.
Stone, Kevin H.
Automated prediction of lattice parameters from X-ray powder diffraction patterns
title Automated prediction of lattice parameters from X-ray powder diffraction patterns
title_full Automated prediction of lattice parameters from X-ray powder diffraction patterns
title_fullStr Automated prediction of lattice parameters from X-ray powder diffraction patterns
title_full_unstemmed Automated prediction of lattice parameters from X-ray powder diffraction patterns
title_short Automated prediction of lattice parameters from X-ray powder diffraction patterns
title_sort automated prediction of lattice parameters from x-ray powder diffraction patterns
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662964/
https://www.ncbi.nlm.nih.gov/pubmed/34963768
http://dx.doi.org/10.1107/S1600576721010840
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