Journal of Applied Crystallography最新文献

The prediction of crystal systems and space groups has been widely used in the estimation of crystalline material properties and structure prediction. Previous research regarding structure determination methods based on X-ray diffraction experiments and density functional theory has attained remarkable efficacy and performance, but these approaches are not applicable to large-scale screening. There are also machine learning models that use Magpie descriptors for space group prediction; for example, that proposed by Liang et al. [Phys. Rev. Mater. (2020), 4, 123802] exhibited prediction accuracies ranging between 0.638 and 0.907 for different types of crystals. Here we put forward a branch network model, named TransMagNet, which is based on transformer encoders [Vaswani et al. (2017). Advances in neural information processing systems 30, pp. 5998–6008] and Magpie linear layers [Ward et al. (2016). npj Comput. Mater.2, 16028], to predict the crystal systems and space groups of materials by merely relying on compositional data. Benchmarking on the Materials Project dataset demonstrates a significant performance improvement in space group classification of our model over previous models, with an accuracy spanning from 0.811 to 0.981 and a maximum improvement in prediction accuracy of 6.5%. The model also achieves a significant performance improvement in crystal system prediction, with an accuracy of 0.854.

In this study, the evolution of residual stress and elastic anisotropy in 17–4 PH stainless steel produced by atomic diffusion additive manufacturing (ADAM) and then subjected to surface mechanical attrition treatment (SMAT) was investigated. Angle- and energy-dispersive X-ray diffraction techniques were employed to analyse the residual stress profiles in both the as-built and SMAT-processed samples. The results reveal that SMAT introduces compressive residual stresses while refining the material subgrain structure. Residual stress analysis indicates that the as-built sample exhibits tensile stresses near the surface, which gradually decrease with depth. In contrast, the SMAT-processed sample shows compressive stresses, ranging from −200 MPa at the surface to −600 MPa in deeper regions. This study highlights the critical role of selecting an appropriate grain-interaction model for X-ray stress factor calculation to ensure accurate residual stress characterization, which is essential for the reliability and performance of additively manufactured components, particularly applications with high-level loading.

Obitutary.

We report the construction and performance of the small-angle neutron scattering instrument ib-SAS at the compact accelerator-based neutron source RANS, RIKEN, Wako, Japan. With this instrument, we aim to increase the opportunities for using neutrons for university education and/or industrial use (e.g. screening for inferior goods). A time-of-flight method, combined with pulsed neutrons with a wide wavelength band from 1 to 10 Å, is necessary to compensate for the weak luminescence of the compact neutron source. Further enhancement has been achieved by employing a multi-pinhole collimator as a converging-beam device; 81 (= 9 × 9) pinholes select thermal neutrons emitted from the large surface area of a solid polyethylene (PE) moderator and produce a focused beam on the detector. To reduce the background originating from stray neutrons in the beam hall of RANS, we keep the path of small-angle scattering in a vacuum and cover it by a thick shield of Cd plates and PE blocks containing B₄C powder. To cover a wide range of length scales d [or wavenumber q (= 2π/d)], three detector blocks (small-angle, wide-angle and backward scattering) were installed on the ib-SAS instrument. The small-angle scattering obtained for glassy carbon and sodium dodecyl sulfate micelle solutions is quantitatively compared with that obtained from the iMATERIA instrument (BL20) at J-PARC, Tokai, with respect to the covered q range and the measurement efficiency and statistics. Similarly to scanning electron microscopy, the SANS instrument at RANS was used to provide a map image showing the water distribution in a mortar plate, the bottom of which was immersed in water. The incoherent scattering from hydrogen was determined and plotted as a function of height.

Wide-angle X-ray diffraction is a crucial technique for probing the nanoscale texture and strain gradient of nanofiber-based composite materials, particularly in determining the 3D orientation distribution of crystalline nanofiber networks. However, extracting 3D orientation information of nanofibers from diffraction patterns remains a significant challenge, especially when dealing with diffraction patterns resulting from multiple fiber sets. Here we introduce Restrfcn, an end-to-end framework which integrates a transformer encoder with a fully connected network through residual connection. We demonstrate its capability in extracting fiber orientation parameters even when the number of nanofiber sets is a variable. To eliminate ineffective neurons in the network, which can simplify the architecture and enhance the model's fitting performance, the Restrfcn model is optimized by using a statistical hypothesis testing method. The deployment of Restrfcn has significant potential for providing real-time data analysis in high-throughput and multi-dimensional synchrotron diffraction experiments.