TY - JOUR A1 - Cakir, Cafer Tufan A1 - Bogoclu, Can A1 - Emmerling, Franziska A1 - Streli, Christina A1 - Guilherme Buzanich, Ana A1 - Radtke, Martin T1 - Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach JF - Machine Learning: Science and Technology N2 - In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup. Our results show that this optimized approach maintains data quality while significantly improving efficiency, making GE-XANES more accessible to a wider range of materials science applications. KW - Machine Learning KW - GE-XANES KW - Bayesian Optimization PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-603955 DO - https://doi.org/10.1088/2632-2153/ad4253 VL - 5 IS - 2 SP - 1 EP - 12 PB - IOP Publishing AN - OPUS4-60395 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -