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Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. OurMachineLearning basedMotifExtractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.

 

Using Pair Distribution Function (PDF) analysis of in situ total scattering data, we investigate the formation of tungsten and niobium oxides in a simple solvothermal synthesis. We use Pearson Correlation Coefficient (PCC) analysis of the time resolved PDFs to both map the structural changes taking place throughout the synthesis and identify structural models for precursor and product through PCC‐based structure mining. Our analysis first shows that ultra‐small tungsten and niobium oxide nanoparticles form instantaneously upon heating, with sizes between 1.5 and 2 nm. We show that the main structural motifs in the nanoparticles can be described with structures containing pentagonal columns, which is characteristic for many bulk tungsten and niobium oxides. We furthermore elucidate the structure of the precursor complex as clusters of octahedra with O‐ and Cl‐ligands. The PCC based methodology automates the structure characterization and proves useful for analysis of large datasets of for example, time resolved X‐ray scattering studies. The PCC is implemented in ‘PDF in the cloud’, a web platform for PDF analysis.

 

This article reports the study of algorithms for non-negative matrix factorization (NMF) in various applications involving smoothly varying data such as time or temperature series diffraction data on a dense grid of points. Utilizing the continual nature of the data, a fast two-stage algorithm is developed for highly efficient and accurate NMF. In the first stage, an alternating non-negative least-squares framework is used in combination with the active set method with a warm-start strategy for the solution of subproblems. In the second stage, an interior point method is adopted to accelerate the local convergence. The convergence of the proposed algorithm is proved. The new algorithm is compared with some existing algorithms in benchmark tests using both real-world data and synthetic data. The results demonstrate the advantage of the algorithm in finding high-precision solutions.

 

We present a deep learning algorithm, DeepStruc, that can solve a simple nanoparticle structure directly from an experimental Pair Distribution Function (PDF) by using a conditional variational autoencoder.

 

Structural modelling of octahedral tilts in perovskites is typically carried out using the symmetry constraints of the resulting space group. In most cases, this introduces more degrees of freedom than those strictly necessary to describe only the octahedral tilts. It can therefore be a challenge to disentangle the octahedral tilts from other structural distortions such as cation displacements and octahedral distortions. This paper reports the development of constraints for modelling pure octahedral tilts and implementation of the constraints in diffpy-CMI , a powerful package to analyse pair distribution function (PDF) data. The model in the program allows features in the PDF that come from rigid tilts to be separated from non-rigid relaxations, providing an intuitive picture of the tilting. The model has many fewer refinable variables than the unconstrained space group fits and provides robust and stable refinements of the tilt components. It further demonstrates the use of the model on the canonical tilted perovskite CaTiO 3 which has the known Glazer tilt system α + β − β − . The Glazer model fits comparably to the corresponding space-group model Pnma below r = 14 Å and becomes progressively worse than the space-group model at higher r due to non-rigid distortions in the real material. 

Abstract Two different types of monoclinic HfO 2 nanocrystals were employed in this work to study the effect of nanocrystal shape and crystallinity on the structural defects in the YBa2Cu3O7−δ (YBCO) matrix as it leads to an enhancement of pinning performances of solution-derived YBCO nanocomposite films. In this work the nanorod-like HfO 2 nanocrystals obtained from surfactant-controlled synthesis led to short intergrowths surrounding the particles, while spherical HfO 2 nanocrystals from the solvent-controlled synthesis led to the formation of long stacking faults in the YBCO matrix. It means that the small difference in crystallinity, lattice parameters, nanocrystal structures, core diameter of preformed nanocrystals in colloidal solutions have a strong influence on the formation of the structural defects around the particles in the YBCO matrix, leading to different pinning performances. 

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this work, the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters r max , Q max , Q damp and atomic displacement parameters.