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The twist-bend nematic liquid crystal phase is a three-dimensional fluid in which achiral bent molecules spontaneously form an orientationally ordered, macroscopically chiral, heliconical winding of a ten nanometer-scale pitch in the absence of positional ordering. Here, the structure of the twist-bend phase of the bent dimer CB7CB and its mixtures with 5CB is characterized, revealing a hidden invariance of the self-assembly of the twist-bend structure of CB7CB, such that over a wide range of concentrations and temperatures, the helix pitch and cone angle change as if the ground state for a pitch of the TB helix is an inextensible heliconical ribbon along the contour formed by following the local molecular long axis (the director). Remarkably, the distance along the length for a single turn of this helix is given by 2πRmol, where Rmol is the radius of bend curvature of a single all-trans CB7CB molecule. This relationship emerges from frustrated steric packing due to the bent molecular shape: space in the fluid that is hard to fill attracts the most flexible molecular subcomponents, a theme of nanosegregation that generates self-assembled, oligomer-like correlations of interlocking bent molecules in the form of a brickwork-like tiling of pairs of molecular strands into duplex double-helical chains. At higher temperatures in the twist-bend phase, the cone angle is small, the director contour is nearly along the helix axis z, and the duplex chains are sequences of biaxial elements formed by overlapping half-molecule pairs, with an approximately 45° rotation of the biaxis between each such element along the chain.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the MLExchange project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.

Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109-1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.

Cellulose, the most abundant biopolymer on earth, is a versatile, energy rich material found in the cell walls of plants, bacteria, algae, and tunicates. It is well established that cellulose is crystalline, although the orientational order of cellulose crystallites normal to the plane of the cell wall has not been characterized. A preferred orientational alignment of cellulose crystals could be an important determinant of the mechanical properties of the cell wall and of cellulose-cellulose and cellulose-matrix interactions. Here, the crystalline structures of cellulose in primary cell walls of onion (Allium cepa), the model eudicot Arabidopsis (Arabidopsis thaliana), and moss (Physcomitrella patens) were examined through grazing incidence wide angle X-ray scattering (GIWAXS). We find that GIWAXS can decouple diffraction from cellulose and epicuticular wax crystals in cell walls. Pole figures constructed from a combination of GIWAXS and X-ray rocking scans reveal that cellulose crystals have a preferred crystallographic orientation with the (200) and (110)/([Formula: see text]) planes preferentially stacked parallel to the cell wall. This orientational ordering of cellulose crystals, termed texturing in materials science, represents a previously unreported measure of cellulose organization and contradicts the predominant hypothesis of twisting of microfibrils in plant primary cell walls.

Ptychography has rapidly grown in the fields of X-ray and electron imagingfor its unprecedented ability to achieve nano or atomic scale resolution whilesimultaneously retrieving chemical or magnetic information from a sample. Aptychographic reconstruction is achieved by means of solving a complex inverseproblem that imposes constraints both on the acquisition and on the analysis ofthe data, which typically precludes real-time imaging due to computational costinvolved in solving this inverse problem. In this work we propose PtychoNN, anovel approach to solve the ptychography reconstruction problem based on deepconvolutional neural networks. We demonstrate how the proposed method can beused to predict real-space structure and phase at each scan point solely fromthe corresponding far-field diffraction data. The presented results demonstratehow PtychoNN can effectively be used on experimental data, being able togenerate high quality reconstructions of a sample up to hundreds of timesfaster than state-of-the-art ptychography reconstruction solutions oncetrained. By surpassing the typical constraints of iterative model-basedmethods, we can significantly relax the data acquisition sampling conditionsand produce equally satisfactory reconstructions. Besides drasticallyaccelerating acquisition and analysis, this capability can enable new imagingscenarios that were not possible before, in cases of dose sensitive, dynamicand extremely voluminous samples.

The twist-bend nematic liquid crystal phase is a three-dimensional fluid in which achiral bent molecules spontaneously form an orientationally ordered, macroscopically chiral, heliconical winding of a ten nanometer-scale pitch in the absence of positional ordering. Here, the structure of the twist-bend phase of the bent dimer CB7CB and its mixtures with 5CB is characterized, revealing a hidden invariance of the self-assembly of the twist-bend structure of CB7CB, such that over a wide range of concentrations and temperatures, the helix pitch and cone angle change as if the ground state for a pitch of the TB helix is an inextensible heliconical ribbon along the contour formed by following the local molecular long axis (the director). Remarkably, the distance along the length for a single turn of this helix is given by 2πRmol, where Rmol is the radius of bend curvature of a single all-trans CB7CB molecule. This relationship emerges from frustrated steric packing due to the bent molecular shape: space in the fluid that is hard to fill attracts the most flexible molecular subcomponents, a theme of nanosegregation that generates self-assembled, oligomer-like correlations of interlocking bent molecules in the form of a brickwork-like tiling of pairs of molecular strands into duplex double-helical chains. At higher temperatures in the twist-bend phase, the cone angle is small, the director contour is nearly along the helix axis z, and the duplex chains are sequences of biaxial elements formed by overlapping half-molecule pairs, with an approximately 45° rotation of the biaxis between each such element along the chain.

Synchrotron light sources, arguably among the most powerful tools of modern scientific discovery, are presently undergoing a major transformation to provide orders of magnitude higher brightness and transverse coherence enabling the most demanding experiments. In these experiments, overall source stability will soon be limited by achievable levels of electron beam size stability, presently on the order of several microns, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position and current. Until now source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements, periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time how the application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation. Such feed-forward correction based on a neural network that can be continuously online retrained achieves source size stability as low as 0.2  μm (0.4%) rms, which results in overall source stability approaching the subpercent noise floor of the most sensitive experiments.

The semiconductor industry is continuously pushing the limits of photolithography, with feature sizes now smaller than 10 nm. To ensure quality, it has become necessary to look beyond the conventional metrological techniques. X-ray scattering has emerged as a possible contender to determine the average shape of a line grating with subnanometer precision. However, to fulfill its promise, faster algorithms must also be developed to interpret and extract metrics from reciprocal-space scattering data. In this paper, we present a fast and accurate x-ray technique and analysis algorithm: critical-dimension grazing-incidence small-angle x-ray scattering (CD GISAXS). The CD GISAXS technique is used in grazing-incidence configuration with a continuous azimuthal rotation of the sample, and thus does not require high-energy x-rays to penetrate the wafer and greatly reduces the data-acquisition times, permitting analysis within the framework of the distorted-wave Born approximation.

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1H chemical shifts that is comparable to the error between the ab initio results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for 1H , as well as indicating that chemical shift prediction for 13C, 15N, and 17O, which have far fewer training environments than the 1H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

Fully conjugated donor-acceptor block copolymers can serve as the active layer in organic photovoltaics and other organic electronic devices. Self-assembly into periodic domains and crystallization of the constituent blocks are crucial to enable control of the multiscale structure and consequently electronic properties. Resonant soft X-ray scattering (RSoXS) is an invaluable tool to characterize such materials, where tuning the X-ray energy and polarization reveals molecular orientation and domain spacing. Here, anisotropic soft X-ray scattering data reveal the type and degree of orientation within conjugated block copolymers composed of poly(3-hexylthiophene-2,5-diyl) and various push-pull copolymers. The maximum anisotropy is observed at the scattering vector corresponding to the spacing between block copolymer domains, which scales with the end-to-end distance of the blocks, as expected. Furthermore, the anisotropy in RSoXS data reveals that the crystalline P3HT blocks orient, on average, parallel or nearly parallel to the block copolymer interface; the average tilt angle between P3HT chains and domain interfaces can be extracted from the dependence of the anisotropy with polar angle. We interpret this angle to correspond to the average tilt of rings in the unit cell, potentially within a chiral mesostructure. Results are corroborated with scattering calculations based on simple model structures.

Extreme ultraviolet (EUV) lithography is one of the most promising printing techniques for high-volume semiconductor manufacturing at the 14-nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials, such as the exposure-dose sensitivity or the line-width roughness, continue to impede its full adoption into industrial nanofab facilities. Metrology tools are required to address these challenges by helping to assess the impact of the EUV materials' properties and processing conditions along different steps of the nanofabrication process. We apply the resonant soft x-ray scattering (RSoXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon K-edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with subnanometer precision. Critical-dimension grazing-incidence small-angle x-ray scattering is then performed at energies where the contrast is maximized, enabling the reconstruction of the three-dimensional shape of the latent image. We demonstrate the potential of RSoXS to provide a high-resolution height-sensitive profile of patterned EUV resists, which will help in quantifying the evolution of critical features, such as the line-edge roughness, at a key step of the nanofabrication process.

Extreme ultraviolet (EUV) lithography is one of the most promising printing techniques for high-volume semiconductor manufacturing at the 14-nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials, such as the exposure-dose sensitivity or the line-width roughness, continue to impede its full adoption into industrial nanofab facilities. Metrology tools are required to address these challenges by helping to assess the impact of the EUV materials' properties and processing conditions along different steps of the nanofabrication process. We apply the resonant soft x-ray scattering (RSoXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon K-edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with subnanometer precision. Critical-dimension grazing-incidence small-angle x-ray scattering is then performed at energies where the contrast is maximized, enabling the reconstruction of the three-dimensional shape of the latent image. We demonstrate the potential of RSoXS to provide a high-resolution height-sensitive profile of patterned EUV resists, which will help in quantifying the evolution of critical features, such as the line-edge roughness, at a key step of the nanofabrication process.

Thin films of fully conjugated donor-acceptor block copolymers composed of an electron donating block and an electron accepting block can be used as single component photoactive layers in organic photovoltaic (OPV) devices. In order to realize their full potential, control over microphase separation and thin-film morphology are critical. In conjugated block copolymer systems where one or more blocks can crystallize, the morphological evolution is governed by the competition between microphase separation and crystallization. In this work, we control crystallization of fully conjugated block copolymers with a random copolymer block. We suppress the crystal packing of poly(3-hexylthiophene-2,5-diyl) (P3HT) through the insertion of a small number of 3-octylthiophene (3OT) units within the chains, yielding poly(3-hexylthiophene-2,5-diyl-random-3-octylthiophene-2,5-diyl) (P[3HT-r-3OT]). While crystallization of P3HT dominates the morphology and prevents microphase separation in poly(3-hexylthiophene-2,5-diyl)-block-poly((9,9-dioctylfluorene-2,7-diyl)-alt-(4,7-di(thiophene-2-yl)-2,1,3-benzothiadiazole)-5′,5″-diyl) (P3HT-b-PFTBT), modest levels of 3OT suppress crystallization in P[3HT-r-3OT]-b-PFTBT, and permit microphase separation. Thus, we demonstrate that incorporating a random copolymer into a donor-acceptor block copolymer can increase control over microphase separation and lead to enhanced performance in OPV devices.

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Extreme ultraviolet lithography is one of the most promising printing techniques for high volume semiconductor manufacturing at the 14 nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials such as the exposure-dose sensitivity or the line-width roughness continue to impede the full adoption into industrial nanofab facilities. New metrology tools are required to address these challenges by helping to determine the impact of the EUV materials' properties and processing conditions on the roughness through the different step of the process. Here, we apply the resonant soft x-ray scattering (RSOXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon absorption edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with sub-nm precision. Critical-dimension grazing incidence small-angle X-ray scattering (CDGISAXS) was then performed at energies where the contrast was maximized, enabling the reconstruction of the 3D shape of the latent image. This demonstrates the potential of RSOXS to provide a high-resolution heightsensitive profile of patterned EUV resists, which will help to quantify the evolution of critical features, such as the line edge roughness, at each step of the nanofabrication process.

Extreme ultraviolet lithography is one of the most promising printing techniques for high volume semiconductor manufacturing at the 14 nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials such as the exposure-dose sensitivity or the line-width roughness continue to impede the full adoption into industrial nanofab facilities. New metrology tools are required to address these challenges by helping to determine the impact of the EUV materials' properties and processing conditions on the roughness through the different step of the process. Here, we apply the resonant soft x-ray scattering (RSOXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon absorption edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with sub-nm precision. Critical-dimension grazing incidence small-angle X-ray scattering (CDGISAXS) was then performed at energies where the contrast was maximized, enabling the reconstruction of the 3D shape of the latent image. This demonstrates the potential of RSOXS to provide a high-resolution heightsensitive profile of patterned EUV resists, which will help to quantify the evolution of critical features, such as the line edge roughness, at each step of the nanofabrication process.

Electron and X-ray interactions with matter can be recorded as digital images, which are signal acquisition mechanisms often used to investigate materials microstructure. Recently, the ability to quickly acquire large datasets at high resolution has created new challenges in areas that rely upon image-based information. The proposed analysis schemes employ Convolutional Neural Networks as the core algorithm in the reconnaissance of expected events from data gathered in two regimes: experimentally and by simulation. At the interface of physical and digital datasets, we propose classification schemes that exploit complex geometrical structure from scientific images through different machine learning packages, such as MatConvNet and TensorFlow. Our results show correct classification rates over 90% considering thousands of samples from four image modalities: cryo-electron microscopy, X-ray diffraction, X-ray scattering and X-ray microtomography. Our main contributions are: (a) developing algorithms designed for data that stem from physical experiments; (b) building new software to constrain parameter space, particularly given new hardware; and (c) testing different CNN models for classification of scientific images.

Hybrid lead halide perovskites have emerged as high-performance photovoltaic materials with their extraordinary optoelectronic properties. In particular, the remarkable device efficiency is strongly influenced by the perovskite crystallinity and the film morphology. Here, we investigate the perovskites crystallisation kinetics and growth mechanism in real time from liquid precursor continually to the final uniform film. We utilize some advanced in situ characterisation techniques including synchrotron-based grazing incident X-ray diffraction to observe crystal structure and chemical transition of perovskites. The nano-assemble model from perovskite intermediated [PbI6]4- cage nanoparticles to bulk polycrystals is proposed to understand perovskites formation at a molecular- or nano-level. A crystallisation-depletion mechanism is developed to elucidate the periodic crystallisation and the kinetically trapped morphology at a mesoscopic level. Based on these in situ dynamics studies, the whole process of the perovskites formation and transformation from the molecular to the microstructure over relevant temperature and time scales is successfully demonstrated.

In order to tailor the assembling of polymers and organic molecules, a deeper understanding of the kinetics involved in thin film production is necessary. While post-production characterization only provides insight on the final film structure, more sophisticated experimental setups are needed to probe the structure formation processes in situ during deposition. The drying kinetics of a deposited organic thin film strongly influences the assembling process on the nanometer scale. This work presents an experimental setup that enables fine control of the atmosphere composition surrounding the sample during slot die coating, while simultaneously probing the film formation kinetics using in situ grazing incidence X-ray scattering and spectroscopy.