Diffuse single-crystal scattering corrected for molecular form factor effects

Schmidt, Ella; Neder, Reinhard B.

doi:10.1107/s2053273317002297

Cited by 8 publications

(14 citation statements)

References 35 publications

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“…Atom This molecule has been used previously by a few members of the diffuse scattering community to test their analysis methods [12,13]. The data used here were kindly provided by Arkadiy Simonov, in the form of pre-processed hkx planes [13].…”

Section: Tris-tert-butyl-135-benzene Tricarboxamidementioning

confidence: 99%

“…1 illustrates this factorization. Schmidt & Neder (2017) showed that, in such systems, I SRO can be obtained by dividing the diffuse scattering by the known form factor difference squared, or, in the case where this is not known, by dividing by an average form factor squared. The resulting function can be projected into one reciprocal-space unit cell, and the Warren-Cowley SRO parameters (Warren et al, 1951) can be extracted directly from it through a least-squares refinement, providing a quantitative description of local correlations.…”

Section: Introductionmentioning

confidence: 99%

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Unravelling the components of diffuse scattering using deep learning

Fuller,

Rudden

2024

Int Union Crystallogr J

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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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Section: Tris-tert-butyl-135-benzene Tricarboxamidementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unravelling the components of diffuse scattering using deep learning

Fuller,

Rudden

2024

Int Union Crystallogr J

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“…Quantitative refinement of the 2D-ÁPDF is possible using the YELL code (Simonov et al, 2014b). We used a suitably customized version (Schmidt & Neder, 2017) to refine WC correlation parameters for nearest neighbours; by exploiting various symmetry relations there are just three independent parameters to be determined (full details are given in the supporting information). These parameters effectively define the probabilities of different tile pairs, and our results correspond to a better-than- RMC -but not yet perfect -observation of the original matching rules: the rules are obeyed 87.6 (5)% and 76.8 (57)% of the time for correlations extracted from neutron and X-ray data, respectively.…”

Section: 13mentioning

confidence: 99%

Efficient fitting of single-crystal diffuse scattering in interaction space: a mean-field approach

Schmidt

Bulled

Goodwin

2021

Int Union Crystallogr J

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The diffraction patterns of crystalline materials with strongly correlated disorder are characterized by the presence of structured diffuse scattering. Conventional analysis approaches generally seek to interpret this scattering either atomistically or in terms of pairwise (Warren–Cowley) correlation parameters. Here it is demonstrated how a mean-field methodology allows efficient fitting of diffuse scattering directly in terms of a microscopic interaction model. In this way the approach gives as its output the underlying physics responsible for correlated disorder. Moreover, the use of a very small number of parameters during fitting renders the approach surprisingly robust to data incompleteness, a particular advantage when seeking to interpret single-crystal diffuse scattering measured in complex sample environments. As the basis of this proof-of-concept study, a toy model is used based on strongly correlated disorder in diammine mercury(II) halides.

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“…Quantitative refinement of the 2D-∆PDF is possible using the YELL code 45 . We used a suitably customised version 46 to refine Warren-Cowley correlation parameters for nearest-neighbours; by exploiting various symmetry relations there are just three independent parameters to be determined (full details are given in the supplementary information). These parameters effectively define the probability of different tile-pairs, and our results correspond to a better-than-RMC -but not yet perfect -observation of the original matching rules: these rules being obeyed 87.6(5)% and 76.8(57)% of the time for neutron and X-ray data, respectively.…”

Section: Comparison With Established Refinement Approachesmentioning

confidence: 99%

Efficient fitting of single-crystal diffuse scattering in interaction space: a mean-field approach

Schmidt¹,

Bulled²,

Goodwin³

2021

Preprint

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The diffraction patterns of crystalline materials with strongly-correlated disorder are characterised by the presence of structured diffuse scattering. Conventional analysis approaches generally seek to interpret this scattering either atomistically or in terms of pairwise (Warren-Cowley) correlation parameters. Here we demonstrate how a mean-field methodology allows efficient fitting of diffuse scattering directly in terms of a microscopic interaction model. In this way the approach gives as its output the underlying physics responsible for correlated disorder. Moreover, the use of a very small number of parameters during fitting renders the approach surprisingly robust to data incompleteness, a particular advantage when seeking to interpret single-crystal diffuse scattering measured in complex sample environments. We use as the basis of our proof-of-concept study a toy model based on strongly-correlated disorder in diammine mercury(II) halides.

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Diffuse single-crystal scattering corrected for molecular form factor effects

Cited by 8 publications

References 35 publications

Unravelling the components of diffuse scattering using deep learning

Unravelling the components of diffuse scattering using deep learning

Efficient fitting of single-crystal diffuse scattering in interaction space: a mean-field approach

Efficient fitting of single-crystal diffuse scattering in interaction space: a mean-field approach

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