Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784613543446839296 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8662964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chitturisathyar automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT ratnerdaniel automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT walrothrichardc automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT thampyvivek automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT reedevanj automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT dunnemike automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT tassonechristopherj automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns AT stonekevinh automatedpredictionoflatticeparametersfromxraypowderdiffractionpatterns |