TY - CONF A1 - Helmerich, Rosemarie T1 - Active Thermography N2 - Active thermography is a nondestructive testing method for identification of near surface defects and signs of early deterioration. The presentation explains the potential and Limits of the method and the equipment for application to concrete structures. Advanced data analysis can increase the quality of imaging of the measurement. The method is usually applied once for analysis of the current condition. If applied periodically, the method can be used for observation of a deterioration process. T2 - NDT&E Advanced Training Workshop 2016 CY - Berlin, Germany DA - 14.07.2016 KW - Active thermography KW - Nondestructive testing KW - Data analysis KW - Imaging KW - Bond defect detection KW - Defect localization KW - Automated scanning PY - 2016 AN - OPUS4-38478 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph T1 - Challenges in Intelligent Computing for Non destructive testing in civil engineering – today and tomorrow N2 - A large amount of data and information is collected in the field of non-destructive testing (NDT) in civil engineering. The weakly structured data are usually evaluated with regard to specific testing tasks (e.g. geometry determination, damage localization, quality assurance). While the data offers great economic potential, i.e. to support construction planning, monitoring and maintenance processes, the evaluation is manual and case-by-case and therefore too inefficient for broader applications. We present recent visions and approaches how these large amounts of data need to be handled in the future and how we aim to make the acquired knowledge accessible to our stakeholders. Building on initiatives in materials research, we stress the importance of further research in the field of semantic data integration particularly motivate why an ontology is needed for the area of NDT in civil engineering. T2 - EG-ICE 2020 CY - Online meeting DA - 01.07.2020 KW - Data analysis KW - Non destructive testing KW - Computational engineering KW - AI PY - 2020 AN - OPUS4-50971 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Smales, Glen Jacob A1 - Pauw, Brian Richard T1 - Introduction to SAXS N2 - A simplified introductions to small-angle scattering (SAXS), to put across the main concepts and not get bogged down in equations. T2 - Better with Scattering workshop 2020 CY - BAM, Berlin, Germany DA - 16.03.2020 KW - SAXS KW - WAXS KW - MOFs KW - Data analysis KW - Nano PY - 2020 UR - https://www.youtube.com/watch?v=_YY9XtQfANk AN - OPUS4-51021 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob T1 - What is in X-ray scattering Data? An overview of features. N2 - During the Better with Scattering 2021 workshop, we discuss what information can be obtained from an X-ray scattering pattern. Examples of structural features and their physical expression in scattering patterns are demonstrated. T2 - Better with Scattering workshop 2021 CY - Online meeting DA - 29.11.2021 KW - Scattering KW - SAXS KW - X-ray scattering KW - Data analysis KW - Data interpretation KW - Fourier transforms PY - 2021 AN - OPUS4-54011 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Accelerated Materials Discovery Based on Data Analysis and Machine Learning N2 - The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials. T2 - PACRIM & GOMD 2021 CY - Online meeting DA - 13.12.2021 KW - Data analysis KW - Machine learning KW - High-throughput computations PY - 2021 AN - OPUS4-54088 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automation in Computational Materials Science N2 - This lecture illustrated the current state of automation in the field of computational materials science based on recent examples from my research. T2 - NOA Lecture Series FSU Jena CY - Jena, Germany DA - 02.11.2021 KW - Data analysis KW - Machine learning KW - High-throughput computations PY - 2021 AN - OPUS4-54089 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven materials discovery and understanding N2 - Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming. The use of, for example, faster chemical heuristics and machine-learned interatomic potentials would allow to consider a much larger number of candidate materials. In addition to DFT based high-throughput searches, the seminar will discuss two possible ways to accelerate high-throughput searches. Using data analysis on the structures and coordination environments of 5000 oxides, we were able to investigate a chemical heuristic – the famous Pauling rules – regarding its usefulness for the fast prediction of stable materials. We have also investigated how machine-learned interatomic potentials can be used to accelerate the prediction of (dynamically) stable materials. The use of these potentials makes vibrational properties accessible in a much faster way than based on DFT. Our results based on a newly developed potential for silicon allotropes showed excellent agreement with DFT reference data (agreement of the frequencies within 0.1-0.2 THz). In addition, we have successfully used high-throughput calculations in the search for new candidate materials for spintronic applications and ferroelectrics T2 - ETSF Webinar CY - Online meeting DA - 25.06.2021 KW - High-throughput computations KW - Data analysis KW - Machine learning PY - 2021 AN - OPUS4-52980 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob T1 - The Human Factor: Results of a Data Analysis Round Robin N2 - How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors? After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples1. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results https://lookingatnothing.com/index.php/archives/3274). Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field. While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak? In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations. T2 - International Small-Angle Scattering Conference (SAS2022) CY - Campinas, Brazil DA - 11.09.2022 KW - Round robin KW - Data analysis KW - Small angle scattering KW - Nanomaterials KW - MOUSE KW - Interlaboratory comparison KW - Intercomparability KW - Reliability KW - Data analysis round robin KW - Large number of participants PY - 2022 AN - OPUS4-55762 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -