Chloe A. Fuller , Lucas S. P. Rudden , V. T. Forsyth (Editor)
{"title":"利用深度学习揭开漫射散射的组成部分。","authors":"Chloe A. Fuller , Lucas S. P. Rudden , V. T. Forsyth (Editor)","doi":"10.1107/S2052252523009521","DOIUrl":null,"url":null,"abstract":"<div><p>A deep-learning method is applied to separate the components of diffuse scattering from chemical short-range order and the molecular form factor. The method is validated against a large simulated dataset and further tested on a real example, resulting in output components of sufficient quality to use for quantitative analysis.</p></div><div><p>Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure–property relationships in functional materials requires understanding both the average and the local structures. The latter information is contained within diffuse scattering but is challenging to exploit, particularly in single-crystal systems. Separation of the diffuse scattering into its constituent components can greatly simplify analysis and allows for quantitative parameters describing the disorder to be extracted directly. Here, a deep-learning method, DSFU-Net, is presented based on the Pix2Pix generative adversarial network, which takes a plane of diffuse scattering as input and factorizes it into the contributions from the molecular form factor and the chemical short-range order. DSFU-Net was trained on 198 421 samples of simulated diffuse scattering data and performed extremely well on the unseen simulated validation dataset in this work. On a real experimental example, DSFU-Net successfully reproduced the two components with a quality sufficient to distinguish between similar structural models based on the form factor and to refine short-range-order parameters, achieving values comparable to other established methods. This new approach could streamline the analysis of diffuse scattering as it requires minimal prior knowledge of the system, allows access to both components in seconds and is able to compensate for small regions with missing data. DSFU-Net is freely available for use and represents a first step towards an automated workflow for the analysis of single-crystal diffuse scattering.</p></div>","PeriodicalId":14775,"journal":{"name":"IUCrJ","volume":"11 1","pages":"Pages 34-44"},"PeriodicalIF":2.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833394/pdf/","citationCount":"0","resultStr":"{\"title\":\"Unravelling the components of diffuse scattering using deep learning\",\"authors\":\"Chloe A. Fuller , Lucas S. P. Rudden , V. T. Forsyth (Editor)\",\"doi\":\"10.1107/S2052252523009521\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A deep-learning method is applied to separate the components of diffuse scattering from chemical short-range order and the molecular form factor. The method is validated against a large simulated dataset and further tested on a real example, resulting in output components of sufficient quality to use for quantitative analysis.</p></div><div><p>Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure–property relationships in functional materials requires understanding both the average and the local structures. The latter information is contained within diffuse scattering but is challenging to exploit, particularly in single-crystal systems. Separation of the diffuse scattering into its constituent components can greatly simplify analysis and allows for quantitative parameters describing the disorder to be extracted directly. Here, a deep-learning method, DSFU-Net, is presented based on the Pix2Pix generative adversarial network, which takes a plane of diffuse scattering as input and factorizes it into the contributions from the molecular form factor and the chemical short-range order. DSFU-Net was trained on 198 421 samples of simulated diffuse scattering data and performed extremely well on the unseen simulated validation dataset in this work. On a real experimental example, DSFU-Net successfully reproduced the two components with a quality sufficient to distinguish between similar structural models based on the form factor and to refine short-range-order parameters, achieving values comparable to other established methods. This new approach could streamline the analysis of diffuse scattering as it requires minimal prior knowledge of the system, allows access to both components in seconds and is able to compensate for small regions with missing data. DSFU-Net is freely available for use and represents a first step towards an automated workflow for the analysis of single-crystal diffuse scattering.</p></div>\",\"PeriodicalId\":14775,\"journal\":{\"name\":\"IUCrJ\",\"volume\":\"11 1\",\"pages\":\"Pages 34-44\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833394/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUCrJ\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S205225252400006X\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUCrJ","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S205225252400006X","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Unravelling the components of diffuse scattering using deep learning
A deep-learning method is applied to separate the components of diffuse scattering from chemical short-range order and the molecular form factor. The method is validated against a large simulated dataset and further tested on a real example, resulting in output components of sufficient quality to use for quantitative analysis.
Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure–property relationships in functional materials requires understanding both the average and the local structures. The latter information is contained within diffuse scattering but is challenging to exploit, particularly in single-crystal systems. Separation of the diffuse scattering into its constituent components can greatly simplify analysis and allows for quantitative parameters describing the disorder to be extracted directly. Here, a deep-learning method, DSFU-Net, is presented based on the Pix2Pix generative adversarial network, which takes a plane of diffuse scattering as input and factorizes it into the contributions from the molecular form factor and the chemical short-range order. DSFU-Net was trained on 198 421 samples of simulated diffuse scattering data and performed extremely well on the unseen simulated validation dataset in this work. On a real experimental example, DSFU-Net successfully reproduced the two components with a quality sufficient to distinguish between similar structural models based on the form factor and to refine short-range-order parameters, achieving values comparable to other established methods. This new approach could streamline the analysis of diffuse scattering as it requires minimal prior knowledge of the system, allows access to both components in seconds and is able to compensate for small regions with missing data. DSFU-Net is freely available for use and represents a first step towards an automated workflow for the analysis of single-crystal diffuse scattering.
期刊介绍:
IUCrJ is a new fully open-access peer-reviewed journal from the International Union of Crystallography (IUCr).
The journal will publish high-profile articles on all aspects of the sciences and technologies supported by the IUCr via its commissions, including emerging fields where structural results underpin the science reported in the article. Our aim is to make IUCrJ the natural home for high-quality structural science results. Chemists, biologists, physicists and material scientists will be actively encouraged to report their structural studies in IUCrJ.