issue contents

ISSN: 2053-2733

March 2023 issue

Highlighted illustration

Cover illustration: Crystal structures have long been the main source of information about intermolecular interactions, which are central to many fields of material science including drug formulation and crystal engineering. Initially, the analysis of intermolecular interactions was of a statistical nature, for instance using radial distribution functions [as shown in light red for the Hg/NO3 pair in bis(μ5-nitrato)-(12-mercuracarborand-4)-bis(18-crown-6-potassium)], while now the modern algorithms of machine learning allow the underlying atom pair potentials (dark red) to be derived, as demonstrated by Hofmann & Kuleshova [Acta Cryst. (2023), A79, 132–144].


research papers

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Machine learning was employed on the Cambridge Structural Database to derive a general force field for all observed atom–atom interactions. The force field parameters, i.e. interatomic potentials and `critical bond distances', are derived to calculate the intermolecular Gibbs energy, which is important for the prediction of crystal structures, solubility and other thermodynamic properties.

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Reflection position, size and shape prediction and partiality estimation of crystal diffraction by integrating using a Gaussian basis are described.


research papers

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A new computational program to analyse and extract tilt data from molecular dynamics simulations of perovskites is presented and results compared with experimental data.

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A dynamical theory of X-ray diffraction is presented for a crystal with surface relief operated in a single-mode regime.

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The T-matrix is used to compute the scattering of fast electrons by a regular array of effective spherical potential wells. An assessment of the forward scattering approximation and a real-space multiple scattering interpretation are provided.

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After a brief review of tilings of 3-periodic nets, the use of essential rings is proposed to identify transitive tilings.

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A fast two-stage algorithm developed for highly efficient and accurate non-negative matrix factorization in smoothly varying data, such as atomic pair distribution function data, is reported.

short communications

international union of crystallography

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