TY - CONF A1 - Radtke, Martin T1 - Machine learning - Examples from BAM Line N2 - This talk explores applications of artificial intelligence in the field of spectroscopy, with a special focus on research conducted at BAMline. We begin with an overview of synchrotron radiation and its unique capabilities in material analysis. The first part of the talk will cover the integration of AI algorithms in quantifying X-ray fluorescence measurements, providing enhanced precision and efficiency in data analysis. Following this, we will examine the role of language models in spectroscopic research, showcasing how these tools can facilitate the control of complex experimental set-ups T2 - Analytical Academy CY - Berlin, Germany DA - 12.11.2024 KW - ChatGPT KW - Neural networks KW - XRF KW - Synchrotron KW - BAMline PY - 2024 AN - OPUS4-61698 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin T1 - Insights into materials with hard x-rays: capabilities of the bamline N2 - This contribution provides an overview of the BAMline synchrotron radiation beamline, which specializes in hard X-ray spectroscopy techniques for materials research. The BAMline offers X-ray absorption spectroscopy (XAS), x-ray fluorescence spectroscopy (XRF), and tomography to study materials' electronic structure, chemical composition, and structure. Key capabilities include standard and dispersive XAS for electronic structure, micro-XRF for elemental mapping, coded aperture imaging, and depth-resolved grazing exit XAS. The BAMline enables in situ characterization during materials synthesis and functions for energy, catalysis, corrosion, biology, and cultural heritage applications. Ongoing developments like the implementation of machine learning techniques for experiment optimization and data analysis will be discussed. For instance, Bayesian optimization is being used to improve beamline alignment and scanning. An outlook to the future, where the BAMline will continue pioneering dynamic and multi-scale characterization, aided by advanced data science methods, to provide unique insights into materials research, will be given. T2 - μ-XRF at Elettra 2.0: challenges and opportunities CY - Trieste, Italy DA - 11.09.2023 KW - Synchrotron KW - XRF KW - XANES KW - Bayes PY - 2023 AN - OPUS4-58607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin T1 - Das goldene Zeitalter war damals Analyse von Gold mit Synchrotronstrahlung N2 - Gold ist eines der sieben bereits in der Antike bekannten Metalle und wurde wegen seines Glanzes und seiner Seltenheit seit jeher als Tauschmittel und zur Herstellung von Schmuck verwendet. Außerdem ist es leicht zu bearbeiten und weitgehend resistent gegen chemische Einflüsse. Die Analyse von Gold mit der durch Synchrotronstrahlung angeregten Röntgenfluoreszenzanalyse ist zerstörungsfrei und liefert Informationen über die in der untersuchten Probe vorhandenen chemischen Elemente. Im Mittelpunkt der hier vorgestellten Untersuchungen an der BAMline stehen Fragen nach der Herkunft, dem Herstellungsprozess und der Zugehörigkeit von Goldfunden. Die verschiedenen Fragestellungen werden anhand einer Reihe von Beispielen erläutert, die vom Wikingerschatz von Hiddensee über die Himmelsscheibe von Nebra bis hin zu Funden aus Ägypten reichen. Darüber hinaus werden die heute am Synchrotron verfügbaren modernen Messmethoden vorgestellt. T2 - WISSENSCHAFT MIT WIRKUNG: Workshop Kulturguterhaltung – vom Dampfkessel zu Nanomaterialien CY - Berlin, Germany DA - 17.11.2021 KW - Gold KW - Synchrotron KW - Bernstorf KW - Nebra KW - XRF PY - 2021 AN - OPUS4-54139 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin T1 - Machine learning for direct quantification of XRF measurements N2 - In this talk I’ll describe the use of artificial neural networks (ANN) for quantifying X-ray fluorescence (XRF) measurements. The main idea of this talk is to give an overview of the process needed to generate a model that can then be applied to a specific problem. In XRF, a sample is excited with X-rays and the resulting characteristic radiation is detected to determine elements quantitatively and qualitatively. This is traditionally done in several time-consuming steps. I’ll show the possibilities and problems of using a neural network to realise a "one-click" quantification. This includes generating training data using Monte Carlo simulation and augmenting the existing data set with an ANN to generate more data. The search for the optimal hyperparameters, manually and automatically, is also described. For the case presented, we were able to train a network with a mean absolute error of 0.1% by weight for the synthetic data and 0.7% by weight for a set of experimental data obtained with certified reference materials. T2 - Seminar series: Artificial Intelligence applied to X-ray / synchrotron techniques CY - Online meeting DA - 24.06.2021 KW - Artificial intelligence KW - Machine learning KW - Synchrotron KW - XRF PY - 2021 AN - OPUS4-54140 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin T1 - SRXRF examples from the BAMline N2 - Synchrotron radiation sources with their unique properties in terms of intensity, polarization and adjustability offer a wide range of possibilities in materials research. A basic introduction about the creation and special properties of synchrotron radiation will be given. Examples of current work at BAMline, the high-energy measuring facility of the Federal Institute for Materials Research and Testing at the synchrotron BESSY, are used to illustrate the possibilities and limitations of existing measuring methods. T2 - Better with Scattering CY - Online meeting DA - 16.03.2020 KW - Synchrotron KW - BAMline KW - XRF PY - 2020 AN - OPUS4-51895 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -