Structure analysis of supported disordered molybdenum oxides using pair distribution function analysis and automated cluster modelling

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions

Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model, MLstructureMining, uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2, and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds.

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Structure and Role of a Ga-Promoter in Ni-Based Catalysts for the Selective Hydrogenation of CO2 to Methanol

Supported, bimetallic catalysts have shown great promise for the selective hydrogenation of CO2 to methanol. In this study, we decipher the catalytically active structure of Ni-Ga-based catalysts. To this end, model Ni-Ga-based catalysts, with varying Ni:Ga ratios, were prepared by a surface organometallic chemistry approach. In situ differential pair distribution function (d-PDF) analysis revealed that catalyst activation in H2 leads to the formation of nanoparticles based on a Ni-Ga face-centered cubic (fcc) alloy along with a small quantity of GaOx. Structure refinements of the d-PDF data enabled us to determine the amount of both alloyed Ga and GaOx species. In situ X-ray absorption spectroscopy experiments confirmed the presence of alloyed Ga and GaOx and indicated that alloying with Ga affects the electronic structure of metallic Ni (viz., Niδ-). Both the Ni:Ga ratio in the alloy and the quantity of GaOx are found to minimize methanation and to determine the methanol formation rate and the resulting methanol selectivity. The highest formation rate and methanol selectivity are found for a Ni-Ga alloy having a Ni:Ga ratio of ∼75:25 along with a small quantity of oxidized Ga species (0.14 molNi-1). Furthermore, operando infrared spectroscopy experiments indicate that GaOx species play a role in the stabilization of formate surface intermediates, which are subsequently further hydrogenated to methoxy species and ultimately to methanol. Notably, operando XAS shows that alloying between Ni and Ga is maintained under reaction conditions and is key to attaining a high methanol selectivity (by minimizing CO and CH4 formation), while oxidized Ga species enhance the methanol formation rate.

A reactor for time-resolved X-ray studies of nucleation and growth during solvothermal synthesis

Understanding the nucleation and growth mechanisms of nanocrystals under hydro- and solvothermal conditions is key to tailoring functional nanomaterials. High-energy and high-flux synchrotron radiation is ideal for characterization by powder X-ray diffraction and X-ray total scattering in real time. Different versions of batch-type cell reactors have been employed in this work, exploiting the robustness of polyimide-coated fused quartz tubes with an inner diameter of 0.7 mm, as they can withstand pressures up to 250 bar and temperatures up to 723 K for several hours. Reported here are recent developments of the in situ setups available for general users on the P21.1 beamline at PETRA III and the DanMAX beamline at MAX IV to study nucleation and growth phenomena in solvothermal synthesis. It is shown that data suitable for both reciprocal-space Rietveld refinement and direct-space pair distribution function refinement can be obtained on a timescale of 4 ms.

Atomic structural changes in the formation of transition metal tungstates: the role of polyoxometalate structures in material crystallization

Material nucleation processes are poorly understood; nevertheless, an atomistic understanding of material formation would aid in the design of material synthesis methods. Here, we apply in situ X-ray total scattering experiments with pair distribution function (PDF) analysis to study the hydrothermal synthesis of wolframite-type MWO₄ (M : Mn, Fe, Co, Ni). The data obtained allow the mapping of the material formation pathway in detail. We first show that upon mixing of the aqueous precursors, a crystalline precursor containing [W₈O₂₇]^6- clusters forms for the MnWO₄ synthesis, while amorphous pastes form for the FeWO₄, CoWO₄ and NiWO₄ syntheses. The structure of the amorphous precursors was studied in detail with PDF analysis. Using database structure mining and an automated modelling strategy by applying machine learning, we show that the amorphous precursor structure can be described through polyoxometalate chemistry. A skewed sandwich cluster containing Keggin fragments describes the PDF of the precursor structure well, and the analysis shows that the precursor for FeWO₄ is more ordered than that of CoWO₄ and NiWO₄. Upon heating, the crystalline MnWO₄ precursor quickly converts directly to crystalline MnWO₄, while the amorphous precursors transform into a disordered intermediate phase before the crystalline tungstates appear. Our data show that the more disordered the precursor is, the longer the reaction time required to form crystalline products, and disorder in the precursor phase appears to be a barrier for crystallization. More generally, we see that polyoxometalate chemistry is useful when describing the initial wet-chemical formation of mixed metal oxides.

DeepStruc: towards structure solution from pair distribution function data using deep generative models

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp, fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials.

Structural Changes during the Growth of Atomically Precise Metal Oxido Nanoclusters from Combined Pair Distribution Function and Small-Angle X-ray Scattering Analysis

The combination of in situ pair distribution function (PDF) analysis and small-angle X-ray scattering (SAXS) enables analysis of the formation mechanism of metal oxido nanoclusters and cluster-solvent interactions as they take place. Herein, we demonstrate the method for the formation of clusters with a [Bi38 O45 ] core. Upon dissolution of crystalline [Bi6 O5 (OH)3 (NO3 )5 ]⋅3 H2 O in DMSO, an intermediate rapidly forms, which slowly grows to stable [Bi38 O45 ] clusters. To identify the intermediate, we developed an automated modeling method, where smaller [Bix Oy ] structures based on the [Bi38 O45 ] framework are tested against the data. [Bi22 O26 ] was identified as the main intermediate species, illustrating how combined PDF and SAXS analysis is a powerful tool to gain insight into nucleation on an atomic scale. PDF also provides information on the interaction between nanoclusters and solvent, which is shown to depend on the nature of the ligands on the cluster surface.

Structure determination of organic compounds by a fit to the pair distribution function from scratch without prior indexing

A method for the ab initio crystal structure determination of organic compounds by a fit to the pair distribution function (PDF), without prior knowledge of lattice parameters and space group, has been developed. The method is called 'PDF-Global-Fit' and is implemented by extension of the program FIDEL (fit with deviating lattice parameters). The structure solution is based on a global optimization approach starting from random structural models in selected space groups. No prior indexing of the powder data is needed. The new method requires only the molecular geometry and a carefully determined PDF. The generated random structures are compared with the experimental PDF and ranked by a similarity measure based on cross-correlation functions. The most promising structure candidates are fitted to the experimental PDF data using a restricted simulated annealing structure solution approach within the program TOPAS, followed by a structure refinement against the PDF to identify the correct crystal structure. With the PDF-Global-Fit it is possible to determine the local structure of crystalline and disordered organic materials, as well as to determine the local structure of unindexable powder patterns, such as nanocrystalline samples, by a fit to the PDF. The success of the method is demonstrated using barbituric acid as an example. The crystal structure of barbituric acid form IV solved and refined by the PDF-Global-Fit is in excellent agreement with the published crystal structure data.

There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis

The development of new functional materials builds on an understanding of the intricate relationship between material structure and properties, and structural characterization is a crucial part of materials chemistry. However, elucidating the atomic structure of nanomaterials remains a challenge using conventional diffraction techniques due to the lack of long-range atomic order. Over the past decade, Pair Distribution Function (PDF) analysis of X-ray or neutron total scattering data has become a mature and well-established method capable of giving insight into the atomic structure in nanomaterials. Here, we review the use of PDF analysis and modelling in characterization of a range of different nanomaterials that exhibit unique atomic structure compared to the corresponding bulk materials. A brief introduction to PDF analysis and modelling is given, followed by examples of how essential structural information can be extracted from PDFs using both model-free and advanced modelling methods. We put an emphasis on how the intuitive nature of the PDF can be used for understanding important structural motifs, and on the diversity of applications of PDF analysis to nanostructure problems.