利用深度学习揭开漫射散射的组成部分。

IF 2.9 2区 材料科学 Q2 CHEMISTRY, MULTIDISCIPLINARY
IUCrJ Pub Date : 2024-01-01 DOI:10.1107/S2052252523009521
Chloe A. Fuller , Lucas S. P. Rudden , V. T. Forsyth (Editor)
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引用次数: 0

摘要

许多技术上重要的材料特性是由无序和短期结构相关性支撑的;因此,阐明功能材料的结构-性能关系需要了解平均结构和局部结构。后一种信息包含在漫射散射中,但很难利用,特别是在单晶系统中。将漫射散射分离成其组成分量可以大大简化分析,并允许直接提取描述无序的定量参数。本文提出了一种基于Pix2Pix生成对抗网络的深度学习方法DSFU-Net,该方法将扩散散射平面作为输入,并将其分解为分子形状因子和化学短程阶数的贡献。DSFU-Net在198421个模拟散射数据样本上进行了训练,在未见过的模拟验证数据集上表现非常好。在一个真实的实验实例中,DSFU-Net成功地复制了这两个部件,其质量足以区分基于形状因素的类似结构模型,并改进了短程顺序参数,获得了与其他既定方法相当的值。这种新方法可以简化漫射散射的分析,因为它对系统的先验知识要求最低,可以在几秒钟内访问两个组件,并且能够补偿丢失数据的小区域。DSFU-Net可以免费使用,它代表了迈向单晶漫射散射分析自动化工作流程的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
IUCrJ
IUCrJ CHEMISTRY, MULTIDISCIPLINARYCRYSTALLOGRAPH-CRYSTALLOGRAPHY
CiteScore
7.50
自引率
5.10%
发文量
95
审稿时长
10 weeks
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.
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