Synchrotron light source facilities worldwide are evolving into the fourth generation, equipped with diffraction-limited storage rings. These machines generate high quality X-rays with intense brightness, low emittance, ultrafast pulse, and highly coherent beams, offering extreme spatial and temporal resolving power that enables multiscale and ultra-fast characterizations. Consequently, the...
Bragg Coherent Diffraction Imaging (BCDI) is a powerful X-ray imaging technique to reveal 3D strain distribution of crystalline nanoparticles. The method records the 3D diffraction intensity of a nanoparticle slice by slice by incrementally rotating the sample within a very small angular range. The iterative phase retrieval method will then be employed to phase the sampled 3D diffraction...
Due to the urgent demand for high-energy-density batteries, lithium (Li) metal batteries (LMBs) have garnered increasing attention. However, the development of LMBs has been hindered by limited cycle life and safety concerns arising from side reactions between lithium metal and the electrolyte, as well as the formation of unstable solid electrolyte interfaces. To address this issue, a...
Particularly in laboratory XRD measurements, where the intensities in diffraction experiments tend to be low, an adaption of the exposure time to the investigated microstructure is crucial [1]. An adequately defined counting time is crucial if a high number of measuring points is examined in one measuring cycle. Examples of such cases are texture and residual stress measurements. A counting...
Neutron three-axis spectrometers (TAS) provide the opportunity to model lattice dynamics and magnetic interactions by measuring the energy loss of neutron while interacting with material, and applying physical knowledge on the results. In result, the forces forming chemical structures, the origin of magnetic order and the reasons for hybridized excitation modes can be determined qualitatively...
This presentation focuses on the application of machine learning techniques, specifically deep reinforcement learning, to improve the process of X-ray reflectivity (XRR) measurements. Our study demonstrates how machine learning can be utilized to dynamically adjust measurement angles and integration times, adapting these parameters after acquisition of each new datapoint to optimize the...
In this talk I would like to present an overview of the progress made at Diamond Light Source since the 2023 meeting at the ALS. Since April 2023 we have internally held two workshops in order to come up with a roadmap, which I will discuss, as well as delivering internal training, in collaboration with the Scientific Machine Learning group on our campus.
In addition to this we have...
Due to the high amount of data during a series of GISAXS measurements, automatic binning is helpful for the evaluation by the researcher. This binning supports the confirmation of existing theories or shows outliers where physics and models need to be refined. Such bins represent unique, relevant parameters of the results, also called identity = “collective aspect of the set of characteristics...
Prompt Gamma Activation Analysis (PGAA) is a highly sensitive non-destructive method of chemical analysis with neutrons. PGAA is widely used in various scientific fields, such as archaeometry, materials science, and biomonitoring of air pollution. Prompt gamma-ray spectra contain up to thousands of gamma lines, which results in a laborious and time-consuming post-processing by hand. The...
The co-alignment of multiple individual single crystals is a common practice in mass-sensitive techniques like μSR and inelastic neutron scattering, particularly when limited by the ability to grow larger crystals. This alignment process has historically been labour-intensive and often not very precise (e.g. [1]).
The ALSA device aims to revolutionize this procedure by automating the...
Understanding collective excitations in materials is important for developing the next generation of spintronic devices for information transfer and storage. Excitations are often characterized via the dynamical structure factor, $S(\mathbf{Q}, \omega)$, which can be measured using inelastic neutron or x-ray scattering techniques. Real-time analysis during an experiment is challenging due to...
Modern light sources produce too many signals for a small operations team to monitor in real time. As a result, recovering from faults can require long downtimes, or even worse subtle performance issues may persist undiscovered. Existing automated methods tend to rely on pre-set limits which either miss subtle problems or produce too many false positives. AI methods can solve both problems,...
X-ray chemical tomography methods are non-destructive techniques that provide hyperspectral images of a sample's cross-section. These methods merge spectroscopy or scattering techniques with tomographic data collection, resulting in coloured images containing spatially-resolved physico-chemical information. Each pixel in these reconstructed images corresponds to a complete spectrum or...
Nowadays software for Small Angle Scattering (SAS) data fitting have a large selection of analytical and numerical models to describe the form factor of the scattering objects. It may become overwhelming to choose an adequate model for the data, especially for new users of SNS instruments. In this work, we train a convolutional neural network (CNN) to predict the form factor model on a dataset...
Understanding structure-property relationships in structural materials can only advance with state-of-the-art characterization. Probing the structure by x-rays has only recently become feasible, mostly by advances in nano-focusing. By scanning techniques, diffraction data of many different grains can be collected. My project aims at dealing with the data obtained from such experiments, in...
We investigate new concepts for enhancing the data acquisition efficiency of scanning type instruments exploring a multidimensional feature space. We test machine-learning algorithms and probabilistic methods in order to minimize the number of experimental data points, which are required to determine models and model parameters down to precisions defined by the scientists. Data acquisition,...
Autonomous experimentation (AE) holds enormous promise for accelerating scientific discovery, by leveraging machine-learning to drive experimental loops where the machine selects and conducts experiments. This talk will discuss AE at synchrotron x-ray scattering beamlines. Deep learning is used to classify x-ray detector images, with performance improving when domain-specific data...
Cellulose, a well-known natural biopolymer, possesses numerous advantages such as cost-effectiveness, renewability, ease of processing, and biodegradability [1]. Due to these inherent merits, cellulose has emerged as a promising bio-based substrate capable of synergistically combining with conductive materials (e.g., metals or carbon-based materials) for diverse applications including sensors,...
The past few years have witnessed booming research in machine learning in chemistry and materials sciences. New pharmaceutical molecules and new energy materials have been identified by machine learning, leading to a paradigm shift in research and industry. Quantum materials, on the other hand, despite constant new reports in using machine learning, have experienced significant challenge due...
Artificial intelligence (AI), when interfaced with laboratory automation, can accelerate materials optimization and scientific discovery. For example, it may be used to efficiently map a phase-diagram with intelligent sampling along phase boundaries, or in ‘retrosynthesis’ problems where a material with a target structure is desired but its synthetic route is unknown. These AI-driven...
Very recently, it became possible to combine propagation-based phase contrast-imaging (PCI) and X-ray diffraction at extreme conditions at the Extreme Conditions Beamline (P02.2), PETRA III, DESY, Hamburg. This first platform for such experiments enables the investigation of hierarchical structures at conditions approaching those observed in the internal structure of planets, with pressures...
The core-shell micelles formed by weakly amphiphilic diblock copolymers from poly(2-oxazoline)s (POx) have been shown to be efficient drug carriers [1]. The water solubility of POx is controlled by the nature of the side groups. In the present work, we investigate POx-based molecular brushes, in which diblock copolymers from hydrophilic poly(2-methyl-2-oxazoline) (PMeOx) and weakly hydrophobic...
We present a new approach to the fast optimization of crystal electric field (CEF) parameters to fit experimental data. This approach is implemented in a lightweight Python-based program, CrysFieldExplorer, using Particle-Swarm-Optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES). The main novelty of the method is the development of a unique loss function, referred...
We present the Data Analysis Remote Treatment Service (DARTS) [1,2], an open-source remote desktop service that launches on-demand virtual machines in the cloud, and displays them in a browser. The released environments can be used for scientific data treatment, for example.
DARTS can be deployed and configured within minutes on a server, and can run any virtual machine. The service is...
Grazing-incidence Wide Angle X-ray scattering (GIWAXS) is a key technique for characterizing surface structures of thin films. The method can be used for in-situ experiments monitoring growth and crystallization effects in real-time, but it produces large amounts of data, frequently exceeding the capabilities of traditional data processing methods.
Feature detection in multidimensional X-ray...
Live reconstruction algorithms for ptychographic phase retrieval can enable immediate visual feedback during scanning, allowing for readjustments of the experiment, as well as paving the way for adaptive scanning techniques. We have shown in previous works that live variants of projection-based iterative algorithms, such as the Difference Map, can be naturally derived and may achieve higher...
Event mode data acquisition in neutron and x-ray scattering experiments has already been demonstrated at multiple labs. The main advantage of this technique is that the data reduction is completely flexible after the experiment, so the re-binning of histograms can be tuned to the experimental data. Compared to accumulating histograms in hardware, event mode data acquisition carries orders of...
Organic-inorganic halide perovskites have gained a huge interest in the scientific community owing to their favorable optoelectronic properties combined with their ease of production and abundance of raw materials. [1] In many cases, polycrystalline thin films are used for which thin film crystallinity and morphology are key factors affecting the perovskite’s properties. Various methods have...
X-ray fluorescence spectroscopy and scattering techniques are pivotal in numerous scientific fields, enabling detailed examination of structures ranging from biological tissues to advanced materials. Traditionally, Charge-Coupled Devices (CCDs) and Complementary Metal-Oxide-Semiconductor (CMOS) sensors have been employed extensively in detecting soft and tender X-rays in various X-ray...
As the most essential alternative materials for eco-friendly perovskite solar cells (PSCs), Tin-based perovskites have achieved an efficiency of 14.81%, which is far less than 25.7% of lead-based devices. The main reason is that it is easy to oxidize to Sn$^{4+}$ in the presence of oxygen and water due to the low stability of the Sn$^{2+}$ state. The oxidation of Sn$^{2+}$ will form the Sn (Ⅳ)...
Keywords: Materials Characterization, Diffraction Techniques, Machine Learning, Probabilistic Models, Structural Analysis.
Understanding a material inexorably requires from the determination of its atomic structure by means of neutron and x-rays based diffraction techniques. However, although artificial intelligence has shown valuable help in property-prediction lately [1], previous machine...
In the medical imaging field, DLNs have allowed for many recent advances in imaging processing such as super-resolution and segmentation tasks. Similar applications have been studied in the fields of digital rocks, and Li-ion battery research with super-resolution deep learning models being successfully deployed to enhance the resolution of rock X-ray CT (XCT) images and microscopy images of...
Machine learning-based atomic potentials have become instrumental in forecasting the structure and dynamics of diverse materials. These potentials, claiming "ab initio accuracy with the efficiency of classical force field," raise questions about their true generalizability across a broad spectrum of materials. This generalizability, a key factor in extrapolating learned information to new...
The position that ions occupy in the unit cell of a crystal and in the periodic table of elements, fully determines the physical, chemical and functional properties of materials. Through diffraction experiments, such as X-ray and neutron scattering, it is possible to determine the crystal structure of a material. However, when such experiments are difficult to conduct (e.g. requiring...
DNS is a polarised diffuse neutron scattering instrument with a time-of-flight inelastic scattering option at MLZ. DNS is particularly useful and powerful for unraveling momentum-, energy-, and neutron-polarisation resolved magnetic correlations in complex magnetic materials and exotic quantum magnets.
The crucial part of DNS data processing workflow is data reduction, e.g. correction of...
Supervised machine learning (ML) models are frequently trained on large datasets of physics-based simulations with the aim of being applied for experimental scattering or spectroscopy data analysis. However, ML models trained on simulated data often struggle to perform on experimental data. Two primary challenges are handling data from structures not present in the training database and...
The phase retrieval problem is a non-linear, ill-posed inverse problem. It is also an important step in X-ray imaging, a precursor to the tomographic reconstruction stage. Experiments involving micro and nanometer-sized objects usually have weak absorption and contrast. This is usually the case in most experiments taking place at high-energy big Synchrotron centres like DESY. Hence, retrieving...
Relationships between the structure and function are at the heart of material science based on functional films, which makes the knowledge of how film morphology influences its function essential. Key objectives are understanding the formation during synthesis and deposition processes as well as the degradation and the deformation during operation in devices and external stimuli. Achieving a...
Neutron scattering technique is an ideal tool to observe spin orders and dynamics, primarily governed by the exchange Hamiltonian. Modeling neutron scattering data involves optimizing the Hamiltonian. Traditionally, forward calculations with a proposed Hamiltonian are used to model inelastic or diffuse neutron scattering data, which is achieved by directly fitting the energy and intensity...
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected separately and saved as a descriptive entry in a database enabling unprecedented accuracy of the collected experimental data. Nevertheless, the common data processing pipeline still involves the binning of data to...
Scalable production of the thin film is interesting for the commercialization of these materials. A fundamental understanding of the structure evolution during deposition is of great importance to tailoring the mesostructures. In a diblock copolymer-assisted sol-gel chemistry method, hybrid films of metallic species and polymer are formed with slot die coating. Pure block copolymers are...
Information theory serves as a practical tool for converting intuitive information into quantifiable numerical values. One key measure, Shannon entropy, plays a role in measuring information content within probability distribution functions. Similarly, mutual information proves valuable in determining correlations between variables, even if they are non-linear. Despite its potential,...
Lower critical solution temperature (LCST) polymers have attracted great interest for 3D bioprinting, as they can form a runny solution at room temperature, but a hydrogel at body temperature. In block copolymers featuring LCST blocks, the mechanical properties in the gel state strongly depend on the architecture of the polymer. Here we address an ABC triblock terpolymer and a BABC tetrablock...
In this paper, we present the GRADES team's exploration and implementation of machine learning (ML) techniques at the SOLEIL synchrotron radiation facility in Saclay. Our work encompasses three distinct use cases, each demonstrating the potential of ML to revolutionize data analysis in large-scale photon facilities.
Firstly, we detail the development of an X-ray diffractogram...
Neutron imaging can provide unique contrast mechanisms. In order to yield reliable and reproducible attenuation coefficients for quantification, one needs to fully understand and characterize the experimental setup. One effect that has been largely overlooked in scintillator-camera based neutron imaging systems, is the backlight scattering or back illumination in the detection system itself,...
The design of neutron instruments usually is related to the calculation of radiation beams, and these simulations are normally decoupled from the source since the nuclear reactions that govern the generation of particles in the source are independent of the specific interactions that take place in the beam path. Also, radiation beams are usually transported far away from the source to reduce...
The Helmholtz-Zentrum Hereon is operating imaging beamlines for X-ray tomography (P05 IBL, P07 HEMS) for academic and industrial users at the synchrotron radiation source PETRA III at DESY in Hamburg, Germany. The high X-ray flux density and coherence of synchrotron radiation enable high-resolution in situ/operando tomography experiments. Here, large amounts of 4D data are collected from a...
Machine learning (ML) is emerging as a new tool for many different fields which now span, among the others, chemistry, physics and material science [1,2]. The idea is to use ML algorithms as a powerful machinery to identify, starting from big data analysis, subtle correlations between simple elemental quantities and complex material properties and then use these to predict them. This approach...
To face the ever growing massive and complex data collected on real materials during samples mapping and screening or in situ measurements at synchrotron beamline, fast software assistance with a minimum user input is nowadays required before, during and after the experiments.
On the french CRG IF BM32 beamline at the European Synchrotron (ESRF) X-ray scattering experiments are carried out...
Neutron scattering is a versatile and powerful technique widely used in materials science to gain insights into materials' properties and uncover new materials. However, this method is often expensive and time-consuming, requiring advanced detector technology and complex data reduction and analysis procedures. Machine learning (ML) has opened new avenues for neutron diffraction data reduction...
We are happy to invite you to our half-day satellite workshop "Machine Learning Basics" on Wednesday, April 10th in Garching at the MLZ.
The workshop is designed for participants keen on exploring basic machine learning techniques and their application to neutron data.
Throughout the workshop, you'll gain insights into fundamental concepts, discover practical applications, and apply...
We are happy to invite you to our half-day satellite workshop "Machine Learning Basics" on Wednesday, April 10th in Garching at the MLZ.
The workshop is designed for participants keen on exploring basic machine learning techniques and their application to neutron data.
Throughout the workshop, you'll gain insights into fundamental concepts, discover practical applications, and apply...
Small-angle X-ray scattering (SAXS) is widely used to analyse the shape and size of nanoparticles in solution. A multitude of models describing the SAXS intensity resulting from nanoparticles of various shapes have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis that can be difficult and time-consuming. We...
Histology remains the gold standard for the visualization and study of biological tissue in clinical pathology and biomedical research. However, the typical workflow entails time-consuming sample preparation steps whereby the tissue is first fixed, embedded and sectioned into slices before chemical staining, after which each slice is individually scanned by optical microscope. In comparison,...
Discovering new phases of condensed matter with novel properties is of vital importance for fundamental and applicational research. Classical Monte Carlo simulations are commonly employed to study phases by stochastically sampling states and evaluating physical quantities from such states. Recently, machine learning has proven useful in classifying, identifying, or interpreting datasets from...
The primary objective of the study is to leverage machine learning methodologies to discern the contributions of various cell types within bamboo structure to the observed scattering patterns. This study employs a comprehensive dataset comprising 145 two-dimensional (2D) wide-angle x-ray scattering (WAXS) patterns obtained from a linear scan over a radial slice of a Guadua bamboo specimen,...
A neutron diffraction pattern auto-indexing algorithm based on machine learning was developed and customed for the diffraction pattern collected from China spallation neutron source (CSNS). Over three hundred thousand crystal structures with different symmetries from the Crystallography Open Database generate the neutron diffraction time-of-flight patterns. In addition, the background and...
The neutron powder diffraction data represent a one-dimensional projection of the three-dimensional structural information. Compared to single crystal neutron diffraction, the reduction in data dimension adds complexity to structural determination from neutron powder diffraction data. Structure determination with neutron powder diffraction is predominantly a manual endeavor, requiring...
The intricate and unstable nature of corrosion in iron-based materials, such as in archaeological materials, necessitates advanced non-destructive methods for compositional analysis and phase segmentation. The accurate quantitative clustering of these compounds requires a robust analytical framework capable of delineating the various phases present in the thick and irregular corrosion layers....
Wood is a heterogeneous biological material, which has a hierarchical structure extending from the molecular level to the macroscopic scale. X-ray and neutron scattering methods are particularly suited for studying wood, because they cover a large portion of the structural hierarchy and allow characterization of samples under various conditions. Wide-angle X-ray scattering (WAXS) detects the...
Over the last decade, many European Photon and Neutron (PaN) facilities have adopted open data policies, making their data available for the benefit of the entire scientific community. This open data has a huge potential to be used for machine learning training, if and only if it is machine-accessible and FAIR.
To try and understand where we stand in the PaN community regarding the...
In this talk, I will discuss mapping the inorganic materials that have been reported in the ICSD [1]. This is important for both Materials Genome Initiative (MGI) [2] approaches to finding new materials and for adequately judging the uncertainty in machine learning approaches to structural determination from diffraction data.
We use a measure of structure similarity to determine how...
Neutron scattering allows for quite complicated sample environments with control over the sample conditions, such as temperature, as well as for the presence of strong magnetic fields. The presence of magnets in scattering experiments necessitates a significant amount of material in the structure. The coils of the magnets, outside the direct beam, add more material into the structure and could...
In the study of soft-matter systems, measurements performed in solution using, e.g., small-angle scattering are very important. Information on the size, shape, and dynamics of the system, can be obtained through modeling of small-angle neutron scattering (SANS) and small-angle x-ray scattering (SAXS) experiments. However, some systems can be challenging to model, due to non-conventional...
With the continuous enhancement of experimental capabilities at scientific user facilities, the demand for computational tools that seamlessly guide users through their data lifecycle grows exponentially. These tools play an important role in facilitating the application of machine learning (ML) techniques to accelerate materials discovery. In light of this, MLExchange introduces a...
All-solid-state lithium-ion batteries (ASSLIBs) have received extensive attention as one of the most promising power sources for flexible and wearable electronics. However, the practical application of ASSLBs has been hindered by poor interfacial stability and inferior ionic conductivity. Solid polymer electrolytes (SPEs) exhibit great potential in developing solid-state batteries,...
Neutron time-of-flight (TOF) data at the ORNL Spallation Neutron Source (SNS) contains multidimensional temporal information in diffraction and parameter spaces. The field's current state relies on sequential data reduction and analysis steps, often involving data transfer between different platforms and tools which introduces inefficiencies and hinders the seamless integration of different...
Stimuli-responsive diblock copolymers (DBCPs) have gathered considerable interest for uptake, transport and release processes due to their property alteration upon exposure to external stimuli, such as temperature and light. In this study, DBCPs consisting of two thermoresponsive blocks, each with lower critical solution temperature (LCST) behavior and coil-to-globule transitions at the...
Phase retrieval is an ill-posed inverse problem with several applications in the fields of medical imaging and materials science. Conventional phase retrieval algorithms either simplify the problem by assuming certain object properties and optical propagation regimes or tuning a large number of free parameters. While the latter most often leads to good solutions for a wider application range,...
Prompt-Gamma Activation Analysis (PGAA) measurement facilities for large samples have been intensively researched within the last years. Here, the interaction of the neutron flux field and the sample cannot be neglected. This leads to a nonlinear relation between the peak count rates and the elemental masses. Therefore, it is necessary to use an iterative evaluation procedure in this case....
In this contribution, an overview of experimental results obtained via simultaneous small- and wide-angle X-ray scattering (SWAXS) experiments is given to illustrate its importance in polymer science. Owing to the high spatial and temporal resolution, which can be beyond that of conventional material characterization methods, in situ synchrotron SWAXS experiments are suitable for...
In this contribution, an overview of experimental results obtained via simultaneous small- and wide-angle X-ray scattering (SWAXS) experiments is given to illustrate its importance in polymer science. Owing to the high spatial and temporal resolution, which can be beyond that of conventional material characterization methods, in situ synchrotron SWAXS experiments are suitable for investigating...
The COVID-19 pandemic underscores the urgent need for swift advancements in therapeutic discovery against emerging health threats. Membrane-active peptides (MAPs) are a class of bioactive compounds with diverse applications in antimicrobial activity and drug delivery across cell membranes. Despite their immense potential, the sheer complexity of the space of possible MAPs presents challenges...
During this talk, I will discuss our work [1] to use neural networks to automatically classifiy Bravais lattices and space-groups from
neutron powder diffraction data. Our work classifies 14 Bravais lattices and 144 space groups. The novelty of our approach is to use semi- supervised and self-supervised learning to allow for training on data sets with unlabeled data as is common at user...
Inelastic neutron scattering instruments allow detailed studies of the dynamical structure factor, $S(Q, \omega)$, where $Q$ is a scattering vector in reciprocal space and $h\omega = \Delta E$ an energy transfer. One of the work horses of modern neutron scattering is the triple-axis instrument, which typically have a high neutron flux and good energy resolution.Novel multiplexing triple-axis...
Determination of crystal structures of nano-crystalline, or amorphous compounds is a great challenge in solid states chemistry and physics. Structural analysis using atomic pair distribution function (PDF) of X-ray or neutron total scattering data has the potential to become a very efficient method in this field. Unfortunately, for real-space structure refinements using this method, an initial...
The ultrashort and ultraintense pulses produced by X-ray free-electron lasers (XFELs) realize exposure times that typically lie in the femto- or even attosecond range. One of the long-term goals at free-electron lasers is to develop a diagnostic tool able to characterize the elusive temporal profile of these pulses in real-time and thus open new fields of atto-science with X-rays. In a...
Grazing-incidence small-angle X-ray scattering (GISAXS) is a widely used method for the characterization of the nanostructure of supported thin films and enables time-resolved in situ measurements. The two-dimensional (2D) scattering patterns contain detailed information about the nanostructure within the film and at its surface. Efficient and fast model fitting is often hampered because it is...
Traditionally, the analysis of Laue diffraction pattern, crucial for determining the crystal orientation, has been a time-consuming process, requiring manual input of a skilled user. The development of an fully autonomous recognition tools aims to streamline this procedure, enhance accuracy, and to enable automation of various tasks such as crystal coalignment [1].
Existing Laue...
Efficiently suppressing non-radiative recombination within the hole-blocking layer (HBL) and at the HBL-active layer interface is critical for enhancing solar cell performance. In this study, the TiO$_x$ layer is sputter-deposited onto a SnO$_2$ layer at room temperature as a buried interface modification layer. We investigate the structural evolution of TiO$_x$ during sputter deposition using...
Liquid formulations are ubiquitous, ranging from products such as deicing liquids to food/beverages and biologic drugs. All such products involve precisely tuned composition to enable engineered behaviors, whether that be a drug targeting high-pH tumor areas or a deicing fluid thinning at a specific shear rate so a plane takes off. These engineered responses often involve dozens of...
A method called NAXSUN was developed to measure the effective neutron cross-sections of induced nuclear reactions [1,2,3]. It is based on irradiating multiple samples with energy-wide neutron fluxes and measuring the saturation activity using gamma spectroscopy. Cross section values are then obtained using unfolding techniques. So far, we have used SANDII, GRAVEL and MAXED algorithms for that...
Autonomous experiments rely on the seamless integration of control systems, data acquisition, data processing, and optimization frameworks. However, the inherent variability in facility- or beamline-specific infrastructure components poses a challenge for developing more generalizable setups and presents an obstacle for replication studies and cross-facility experiments.
This project focuses...
Convolutional Neural Networks (CNNs) have emerged as powerful tools in the field of computer vision, demonstrating remarkable capabilities in tasks such as image classification, object detection, and semantic segmentation. Traditional CNNs are primarily designed for processing two-dimensional 2D images. However, many applications, such X-ray tomography and microtomography, involve volumetric...
Experimental Physics and Industrial Control System (EPICS) is a framework for developing distributed control systems. One of the modules available to EPICS is PyDevice which allows connecting python code to the process variables distributed by the EPICS control system. Bluesky is a higher level, user-facing framework for specifying the logic of experiments. In this poster, PyDevice will be...
Neutron residual stress mapping is a valuable tool for determining the bulk residual stress state of large-scale engineering components. Probing the stress state using a high density of measurement points is time intensive and presents a limitation for what is experimentally feasible. These data are traditionally obtained using a brute force approach where data are measured for a discreet grid...
In the exploration of universe and matter, dealing with inverse problems is often a central challenge. In many experimental investigations, which are carried out in particular at large-scale research facilities such as FRM II, DESY or European XFEL, the essential phase information in the experimental data is lost due to the measurement principle (phase problem). Therefore, methods based on...
Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications - such as image restoration - denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles....