$Deep learning for visualization and novelty detection in large X-ray diffraction datasets$
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Deep learning for visualization and novelty detection in large X-ray diffraction datasets

Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns....

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Autores principales:	Lars Banko, Phillip M. Maffettone, Dennis Naujoks, Daniel Olds, Alfred Ludwig
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/4ea8036e8fad48a79945a01a82db4935
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id	oai:doaj.org-article:4ea8036e8fad48a79945a01a82db4935
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spelling	oai:doaj.org-article:4ea8036e8fad48a79945a01a82db49352021-12-02T18:34:13ZDeep learning for visualization and novelty detection in large X-ray diffraction datasets10.1038/s41524-021-00575-92057-3960https://doaj.org/article/4ea8036e8fad48a79945a01a82db49352021-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00575-9https://doaj.org/toc/2057-3960Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.Lars BankoPhillip M. MaffettoneDennis NaujoksDaniel OldsAlfred LudwigNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Lars Banko Phillip M. Maffettone Dennis Naujoks Daniel Olds Alfred Ludwig Deep learning for visualization and novelty detection in large X-ray diffraction datasets
description	Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.
format	article
author	Lars Banko Phillip M. Maffettone Dennis Naujoks Daniel Olds Alfred Ludwig
author_facet	Lars Banko Phillip M. Maffettone Dennis Naujoks Daniel Olds Alfred Ludwig
author_sort	Lars Banko
title	Deep learning for visualization and novelty detection in large X-ray diffraction datasets
title_short	Deep learning for visualization and novelty detection in large X-ray diffraction datasets
title_full	Deep learning for visualization and novelty detection in large X-ray diffraction datasets
title_fullStr	Deep learning for visualization and novelty detection in large X-ray diffraction datasets
title_full_unstemmed	Deep learning for visualization and novelty detection in large X-ray diffraction datasets
title_sort	deep learning for visualization and novelty detection in large x-ray diffraction datasets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ea8036e8fad48a79945a01a82db4935
work_keys_str_mv	AT larsbanko deeplearningforvisualizationandnoveltydetectioninlargexraydiffractiondatasets AT phillipmmaffettone deeplearningforvisualizationandnoveltydetectioninlargexraydiffractiondatasets AT dennisnaujoks deeplearningforvisualizationandnoveltydetectioninlargexraydiffractiondatasets AT danielolds deeplearningforvisualizationandnoveltydetectioninlargexraydiffractiondatasets AT alfredludwig deeplearningforvisualizationandnoveltydetectioninlargexraydiffractiondatasets
_version_	1718377859393781760

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

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