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.

Formation of intermetallic PdIn nanoparticles: influence of surfactants on nanoparticle atomic structure.

Bimetallic nanoparticles have been extensively studied as electrocatalysts due to their superior catalytic activity and selectivity compared to their monometallic counterparts. The properties of bimetallic materials depend on the ordering of the metals in the structure, and to tailor-make materials for specific applications, it is important to be able to control the atomic structure of the materials during synthesis. Here, we study the formation of bimetallic palladium indium nanoparticles to understand how the synthesis parameters and additives used influence the atomic structure of the obtained product. Specifically, we investigate a colloidal synthesis, where oleylamine was used as the main solvent while the effect of two surfactants, oleic acid (OA) and trioctylphosphine (TOP) was studied. We found that without TOP included in the synthesis, a Pd-rich intermetallic phase with the Pd3In structure initially formed, which transformed into large NPs of the CsCl-structured PdIn phase. When TOP was included, the syntheses yielded both In2O3 and Pd3In. In situ X-ray total scattering with Pair Distribution Function analysis was used to study the formation process of PdIn bimetallic NPs. Our results highlight how seemingly subtle changes to material synthesis methods can have a large influence on the product atomic structure.

Influence of the Precursor Structure on the Formation of Tungsten Oxide Polymorphs.

Understanding material nucleation processes is crucial for the development of synthesis pathways for tailormade materials. However, we currently have little knowledge of the influence of the precursor solution structure on the formation pathway of materials. We here use in situ total scattering to show how the precursor solution structure influences which crystal structure is formed during the hydrothermal synthesis of tungsten oxides. We investigate the synthesis of tungsten oxide from the two polyoxometalate salts, ammonium metatungstate, and ammonium paratungstate. In both cases, a hexagonal ammonium tungsten bronze (NH4)0.25WO3 is formed as the final product. If the precursor solution contains metatungstate clusters, this phase forms directly in the hydrothermal synthesis. However, if the paratungstate B cluster is present at the time of crystallization, a metastable intermediate phase in the form of a pyrochlore-type tungsten oxide, WO3·0.5H2O, initially forms. The pyrochlore structure then undergoes a phase transformation into the tungsten bronze phase. Our studies thus experimentally show that the precursor cluster structure present at the moment of crystallization directly influences the formed crystalline phase and suggests that the precursor structure just prior to crystallization can be used as a tool for targeting specific crystalline phases of interest.

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.

Chemical Insights into the Formation of Colloidal Iridium Nanoparticles from In Situ X-ray Total Scattering: Influence of Precursors and Cations on the Reaction Pathway.

Iridium nanoparticles are important catalysts for several chemical and energy conversion reactions. Studies of iridium nanoparticles have also been a key for the development of kinetic models of nanomaterial formation. However, compared to other metals such as gold or platinum, knowledge on the nature of prenucleation species and structural insights into the resultant nanoparticles are missing, especially for nanoparticles obtained from IrxCly precursors investigated here. We use in situ X-ray total scattering (TS) experiments with pair distribution function (PDF) analysis to study a simple, surfactant-free synthesis of colloidal iridium nanoparticles. The reaction is performed in methanol at 50 °C with only a base and an iridium salt as precursor. From different precursor salts─IrCl3, IrCl4, H2IrCl6, or Na2IrCl6─colloidal nanoparticles as small as Ir∼55 are obtained as the final product. The nanoparticles do not show the bulk iridium face-centered cubic (fcc) structure but show decahedral and icosahedral structures. The formation route is highly dependent on the precursor salt used. Using IrCl3 or IrCl4, metallic iridium nanoparticles form rapidly from IrxClyn- complexes, whereas using H2IrCl6 or Na2IrCl6, the iridium nanoparticle formation follows a sudden growth after an induction period and the brief appearance of a crystalline phase. With H2IrCl6, the formation of different Irn (n = 55, 55, 85, and 116) nanoparticles depends on the nature of the cation in the base (LiOH, NaOH, KOH, or CsOH, respectively) and larger particles are obtained with larger cations. As the particles grow, the nanoparticle structure changes from partly icosahedral to decahedral. The results show that the synthesis of iridium nanoparticles from IrxCly is a valuable iridium nanoparticle model system, which can provide new compositional and structural insights into iridium nanoparticle formation and growth.

Breaking with the Principles of Coreduction to Form Stoichiometric Intermetallic PdCu Nanoparticles.

Intermetallic nanoparticles (NPs) have shown enhanced catalytic properties as compared to their disordered alloy counterparts. To advance their use in green energy, it is crucial to understand what controls the formation of intermetallic NPs over alloy structures. By carefully selecting the additives used in NP synthesis, it is here shown that monodisperse, intermetallic PdCu NPs can be synthesized in a controllable manner. Introducing the additives iron(III) chloride and ascorbic acid, both morphological and structural control can be achieved. Combined, these additives provide a synergetic effect resulting in precursor reduction and defect-free growth; ultimately leading to monodisperse, single-crystalline, intermetallic PdCu NPs. Using in situ X-ray total scattering, a hitherto unknown transformation pathway is reported that diverges from the commonly reported coreduction disorder-order transformation. A Cu-rich structure initially forms, which upon the incorporation of Pd(0) and atomic ordering forms intermetallic PdCu NPs. These findings underpin that formation of stoichiometric intermetallic NPs is not limited by standard reduction potential matching and coreduction mechanisms, but is instead driven by changes in the local chemistry. Ultimately, using the local chemistry as a handle to tune the NP structure might open new opportunities to expand the library of intermetallic NPs by exploiting synthesis by design.

Surfactant-free syntheses and pair distribution function analysis of osmium nanoparticles.

A surfactant-free synthesis of precious metal nanoparticles (NPs) performed in alkaline low-boiling-point solvents has been recently reported. Monoalcohols are here investigated as solvents and reducing agents to obtain colloidal Os nanoparticles by using low-temperature (<100 °C) surfactant-free syntheses. The effect of the precursor (OsCl3 or H2OsCl6), precursor concentration (up to 100 mM), solvent (methanol or ethanol), presence or absence of a base (NaOH), and addition of water (0 to 100 vol %) on the resulting nanomaterials is discussed. It is found that no base is required to obtain Os nanoparticles as opposed to the case of Pt or Ir NPs. The robustness of the synthesis for a precursor concentration up to 100 mM allows for the performance of X-ray total scattering with pair distribution function (PDF) analysis, which shows that 1-2 nm hexagonal close packed (hcp) NPs are formed from chain-like [OsO x Cl y ] complexes.

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.

Insights from In Situ Studies on the Early Stages of Platinum Nanoparticle Formation.

Understanding the formation of nanomaterials down to the atomic level is key to rational design of advanced materials. Despite their widespread use and intensive study over the years, the detailed formation mechanism of platinum (Pt) nanoparticles remains challenging to explore and rationalize. Here, various in situ characterization techniques, and in particular X-ray total scattering with pair distribution function (PDF) analysis, are used to follow the structural and chemical changes taking place during a surfactant-free synthesis of Pt nanoparticles in alkaline methanol. Polynuclear structures form at the beginning of the synthesis, and Pt-Pt pair distances are identified before any nanoparticles are generated. The structural motifs best describing the species formed change with time, e.g., from [PtCl5-PtCl5] and [PtCl6-Pt2Cl6-PtCl6] to [Pt2Cl10-Pt3Cl8-Pt2Cl10]. The formation of these polynuclear structures with Pt-Pt coordination before the formation of the nanoparticles is suggested to account for the fast nucleation observed in the synthesis.

A cloud platform for atomic pair distribution function analysis: PDFitc.

A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data (PDFitc) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarityMapping, are described. In the first and second the user uploads a single PDF and the application returns a list of best-fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy-to-use workflow on PDFitc. In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform.

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

Molybdenum oxides and sulfides on various low-cost high-surface-area supports are excellent catalysts for several industrially relevant reactions. The surface layer structure of these materials is, however, difficult to characterize due to small and disordered MoO x domains. Here, it is shown how X-ray total scattering can be applied to gain insights into the structure through differential pair distribution function (d-PDF) analysis, where the scattering signal from the support material is subtracted to obtain structural information on the supported structure. MoO x catalysts supported on alumina nanoparticles and on zeolites are investigated, and it is shown that the structure of the hydrated molybdenum oxide layer is closely related to that of disordered and polydisperse polyoxometalates. By analysing the PDFs with a large number of automatically generated cluster structures, which are constructed in an iterative manner from known polyoxometalate clusters, information is derived on the structural motifs in supported MoO x .

Using Polarized Spectroscopy to Investigate Order in Thin-Films of Ionic Self-Assembled Materials Based on Azo-Dyes.

Three series of ionic self-assembled materials based on anionic azo-dyes and cationic benzalkonium surfactants were synthesized and thin films were prepared by spin-casting. These thin films appear isotropic when investigated with polarized optical microscopy, although they are highly anisotropic. Here, three series of homologous materials were studied to rationalize this observation. Investigating thin films of ordered molecular materials relies to a large extent on advanced experimental methods and large research infrastructure. A statement that in particular is true for thin films with nanoscopic order, where X-ray reflectometry, X-ray and neutron scattering, electron microscopy and atom force microscopy (AFM) has to be used to elucidate film morphology and the underlying molecular structure. Here, the thin films were investigated using AFM, optical microscopy and polarized absorption spectroscopy. It was shown that by using numerical method for treating the polarized absorption spectroscopy data, the molecular structure can be elucidated. Further, it was shown that polarized optical spectroscopy is a general tool that allows determination of the molecular order in thin films. Finally, it was found that full control of thermal history and rigorous control of the ionic self-assembly conditions are required to reproducibly make these materials of high nanoscopic order. Similarly, the conditions for spin-casting are shown to be determining for the overall thin film morphology, while molecular order is maintained.