TY - CONF A1 - Yusenko, Kirill A1 - de Oliveira Guilherme Buzanich, Ana A1 - Radtke, Martin T1 - Studies of high-entropy alloys using x-ray absorption fine structure at the bamline N2 - BAM line is multipurpose high-energy beamline. To extend studies of multicomponent alloys using EXAFS we perform own research and user experiments requiring multiedge spectroscopy, high-temperature and chemically aggressive sample environments. Our study of multicomponent alloys and high-entropy alloys open new perspectives in understanding their reactivity, corrosion, phase transformations and local ordering. T2 - SPP2006: large scale facilities CY - Online meeting DA - 02.11.2021 KW - Synchrotron studies KW - High-entropy alloys KW - EXAFS PY - 2021 AN - OPUS4-54016 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 - George, Janine T1 - Automation in Computational Materials Science N2 - The talk „Automation in computational materials science“ deals with the current state of automation in the field of computational materials science. It illustrates how automation can, for example, be used to speed up the search for new ferroelectric materials and spintronic materials. Furthermore, it lists current tools for automation and challenges in the field. T2 - SALSA School 2021 CY - Online meeting DA - 16.09.2021 KW - Automation KW - High-throughput PY - 2021 AN - OPUS4-53483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Chemical Understanding N2 - This talk presents my research on data-driven chemical understanding to graduate students in chemistry at the Graduate School Chemistry in Paris. T2 - Workshop : Practical applications of Machine Learning in chemistry: perspectives and pitfalls CY - Paris, France DA - 13.07.2022 KW - Chemical Understanding KW - Automation KW - Bonding Analysis PY - 2022 UR - https://gs-chem13.sciencesconf.org/ AN - OPUS4-55410 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Accelerated materials discovery with data analysis and machine learning N2 - Talk on my research on machine-learning and automation for students of TU Berlin T2 - TU Berlin Seminar CY - Online meeting DA - 24.01.2022 KW - Automation KW - High-throughput KW - DFT PY - 2022 AN - OPUS4-54686 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -