Journal of Applied Crystallography最新文献

Material properties can often be derived directly from fundamental equations governing electron behavior. In this study, we present an open-source asymmetric-unit-based graph neural network designed to capture atomic patterns and their corresponding electron distributions. By coarse-graining sites belonging to conjugate subgroups and analyzing reciprocal space through powder X-ray diffraction patterns, our model predicts key physical properties, including formation energy, band gap, bulk modulus and metal/non-metal classification. Our method demonstrates exceptional predictive accuracy for properties calculated using density functional theory across the Materials Project dataset. Our approach is compared with state-of-the-art models and exhibits impressively low error rates in zero-shot predictions.

A comprehensive educational strategy designed to make small-molecule crystallography more accessible for students at various academic levels is described. By integrating hands-on laboratory visits, structured courses and advanced application training, we cultivate a deep understanding of fundamental crystallographic concepts while fostering practical skills. This strategy also aims to inspire novice learners, building their confidence and interest in structural science. Our approach demystifies complex concepts through real-world examples and interactive case-learning modules, enabling students to proficiently apply crystallography in their research. The resulting educational impact is evident in numerous publications from undergraduates, scholarship awards to graduates and successful independent research projects, highlighting the effectiveness of our programme in inspiring the next generation of chemical crystallographers.

The technique of extended-range high-energy-resolution fluorescence detection (XR-HERFD), developed from X-ray absorption spectroscopy, X-ray emission spectroscopy and resonant inelastic X-ray scattering (RIXS), has been used to successfully observe a new X-ray fluorescent satellite in manganese. The experimental methodology, spectral processing and analysis, and how statistical information and structure can be defined, extracted and used from HERFD spectra are detailed. Novel approaches to measure and improve accurate data uncertainty in XR-HERFD, HERFD and RIXS data sets are also presented. This includes definitions of intrinsic resolution and improvements to the resolution of the output and data by a factor of two relative to raw data or standard processing. Novel systematics common in HERFD and RIXS experiments are detailed, including background subtraction and elastic Bragg harmonics, with approaches to dealing with them.

Disordered carbons are of significant scientific and industrial interest for modern applications. To understand the differences in the performance of disordered carbons, it is crucial to elucidate their structure, but this is challenging due to the variation and complexity of structures they can possess: for example, different hybridizations of the carbon atoms, and significant extended-range order composed of connected rings, curved sheets and stacks. This study establishes the useful information that can be obtained from angular correlation analysis of scanning electron nanobeam diffraction patterns for disordered carbons and other materials with extended-range order. The effects of sample thickness and experimental noise are investigated, showing that it is crucial to consider their impact when interpreting the results. Furthermore, opportunities for analyzing different ranges of scattering angles are explored, for example, to access structural information about the intralayer structure of disordered carbons. These approaches could be used to access novel quantitative measures to probe the structural differences of disordered carbons and understand their properties.