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March 2024 issue
Cover illustration: In this issue, Billinge & Proffen [Acta Cryst. (2024), A80, 139–145] introduce a virtual collection of articles from various IUCr journals illustrating the application of artificial intelligence and machine learning in structural science. The cover image was generated by DALL·E using the prompt `A depiction of molecules surrounded by abstract representations of digital data and AI algorithms, highlighting the historical improvements in the data-driven approach to crystallography'.
editorial
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An overview of the virtual collection on machine learning (ML) in crystallography and structural science, as represented in Acta Crystallographica Sections A, B and D, IUCrJ and Journal of Synchrotron Radiation, is presented. Some terms and concepts related to artificial intelligence and machine learning are briefly introduced and described, and a short history of ML in structural science as it appeared in these IUCr journals is given to whet the appetite for the rest of the collection.
advances
research papers
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This paper provides a rapid parameterized calculation of absorptive scattering factors for 103 elements as neutral, spherical atoms, which reduces calculation time considerably.
It is proposed and proved that half of a crystallographic orbit has the same powder X-ray diffraction intensity as its complementary set; three more theorems are deduced. Some structures reported in the Inorganic Crystal Structure Database are further analyzed using this method.
foundations
research papers
The double-slit asymmetrical dynamical diffraction of X-rays in perfect crystals is investigated theoretically. It is shown that interference fringes similar to Young's fringes are formed and the properties of these fringes are investigated as a function of asymmetry angle and other factors.
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A new method is developed for calculating dynamical electron diffraction intensities.
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An experimental and computational investigation is presented of the role of inelastic scattering on electron diffraction intensities.
Symmetry tables of the multiple implication function for all 230 space groups are compiled, including Fourier units. An accurate method for calculation of a symmetry minimum function is also presented.
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The optimal choice of bulk-solvent mask parameters (grid step, and solvent and shrinkage radii) has been revised.
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Many models have been developed for analyzing SAXS data; however choosing the optimal model is difficult and time-consuming, especially for non-expert users. This paper proposes an algorithm, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data.
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An automated high-throughput screening approach is presented for identifying starting structure models for pair distribution function analysis of nanoclusters.
international union of crystallography
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