TY - JOUR A1 - Schmitt, Jan A1 - Seitz, Philipp A1 - Scherdel, Christian A1 - Reichenauer, Gudrun T1 - Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis JF - Computational Materials Science N2 - Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO_2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol–gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network. KW - machine learning KW - neural network KW - autoencoder Y1 - 2023 UR - https://doi.org/10.1016/j.commatsci.2022.111984 SN - 1879-0801 N1 - Link zum Datensatz: https://gitlab.vlab.fm.fhws.de/philipp.seitz/machinelearningandsaxs VL - 218 ER - TY - JOUR A1 - Scherdel, Christian A1 - Miller, Eddi A1 - Reichenauer, Gudrun A1 - Schmitt, Jan T1 - Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML) JF - Processes Y1 - 2021 VL - 9 IS - 4 SP - 672 EP - 672 ER - TY - JOUR A1 - Seitz, Philipp A1 - Scherdel, Christian A1 - Reichenauer, Gudrun A1 - Schmitt, Jan T1 - Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis JF - Computational Materials Science Y1 - 2023 VL - 218 SP - 111984 EP - 111984 ER - TY - JOUR A1 - Wilhelm, Markus A1 - Lotter, Frank A1 - Scherdel, Christian A1 - Schmitt, Jan T1 - Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis JF - Buildings N2 - In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time. KW - Building and Construction KW - Civil and Structural Engineering KW - Architecture Y1 - 2024 U6 - https://doi.org/10.3390/buildings14020340 SN - 2075-5309 VL - 14 IS - 2 PB - MDPI AG ER - TY - JOUR A1 - Wilhelm, Markus A1 - Lotter, Frank A1 - Scherdel, Christian A1 - Schmitt, Jan T1 - Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis JF - buildings N2 - In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-56030 VL - 14 IS - 2 PB - MDPI ER -