Journal of Applied Crystallography最新文献

An innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

Direct methods have practically solved the phase problem for small–medium-size molecules but have substantially failed in macromolecular crystallography. They have two main limitations: a strong dependence on structural complexity and the need to work with atomic-resolution data. Many attempts have been made to broaden their field of applicability, for example the use of some a priori information to make the estimate of the triplet invariant phases more effective. Unfortunately none of these new approaches allowed the successful application of direct methods to proteins and nucleic acids. Direct methods are still a niche tool in macromolecular crystallography. In a recent publication [Giacovazzo (2019). Acta Cryst. A75, 142–157] the method of joint probability distributions has been modified to take into account new sources of prior information, one of which is relevant to this article: the Patterson map. In practice, it has been shown that with prior knowledge of the interatomic vectors one is able to modify the classic Cochran reliability parameter for estimating the triplet invariant phases. The article was essentially theoretical in nature, and no attempt was described to test the practical usefulness of the new probabilistic formulas. This work is therefore the first application of the new method. It is shown that the use of the Patterson map as prior information substantially improves the Cochran estimate of triplet phases; the phase error distribution for the new estimates, even if it is related to macromolecular structures, becomes similar to that obtained for medium-size structures. In some ways, it is as if the use of the Patterson information reduces the structural complexity, thus allowing a more general use of direct methods in macromolecular crystallography. Atomic resolution no longer seems to be a necessary ingredient for the applicability of direct methods; tests show that the apparent reduction in structural complexity also occurs in macromolecular structures with experimental data having a resolution of 2.3 Å. A number of test structures have been used to show the potential of the new technique.

This study considers critical data reduction steps and data analysis approaches required to determine explicitly the atomic arrangements in nanoparticles from time-of-flight neutron total scattering. A practical procedure is described for removing parasitic backgrounds caused by the incoherent scattering of hydrogen inevitably present in most nanoparticle samples and normalizing the recovered coherent scattering intensities onto an absolute scale. A model-free analysis is presented of a pair-distribution function derived from total scattering that can be used to determine thermal expansion coefficients and particle sizes directly from experimental data without knowledge of the material's structure. Finally, atomistic whole-nanoparticle refinements of yttrium-doped ceria nanoparticles from neutron total-scattering data are demonstrated using the reverse Monte Carlo method implemented in the RMCProfile software. These results reveal a strong dependence of the cation–oxygen and oxygen–oxygen distances on the coordination numbers, which leads to gradients of these distances near the particle surface. The details are dependent on the surface coverage by ligands and adsorbates and on the structure of grain boundaries in nanocrystalline agglomerates. The refined models confirm the expectations of more substantial disorder at particle surfaces, with a distorted surface layer extending over several coordination shells. The results highlight the feasibility of whole-nanoparticle refinements from neutron data, calling for further development of data reduction and analysis procedures.