Institut Festkörper- und Materialphysik https://opara.zih.tu-dresden.de/xmlui/handle/123456789/839 2026-03-30T23:50:44Z Python source code, text corpora and automatically generated datasets for Machine Learning https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5903 Python source code, text corpora and automatically generated datasets for Machine Learning Langer, Niklas; Thomas, Andy This is supplementary information to the report: "Entwicklung eines Input-Algorithmus zur Erzeugung von Lerndatensätzen für Maschinelles Lernen mittels Natural Language Recognition" 2023-01-01T00:00:00Z Supplemental material to "Maximal Anderson Localization and Suppression of Surface Plasmons in Two-Dimensional Random Au Networks" https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5814 Supplemental material to "Maximal Anderson Localization and Suppression of Surface Plasmons in Two-Dimensional Random Au Networks" Lubk, Axel; Schultz, Johannes Two-dimensional random metal networks possess unique electrical and optical properties, such as almost total optical transparency and low sheet resistance, which are closely related to their disordered structure. Here we present a detailed experimental and theoretical investigation of their plasmonic properties, revealing Anderson (disorder-driven) localized surface plasmon (LSP) resonances of very large quality factors and spatial localization close to the theoretical maximum, which couple to electromagnetic waves. Moreover, they disappear above a geometry-dependent threshold at ca. 1.6 eV in the investigated Au networks, explaining their large transparencies in the optical spectrum. 2023-01-01T00:00:00Z Data for NCOMMS-22-00582A, Rahn et al. https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5732 Data for NCOMMS-22-00582A, Rahn et al. Rahn, Marein Christopher This dataset contains the necessary data and minimal Matlab scripts to reproduce the Figures in "Kondo quasiparticle dynamics observed by resonant inelastic x-ray scattering" (Rahn et al., 2022) 2022-01-01T00:00:00Z Training and Predicted Data for Machine Learning on Thermoelectric Materials https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5652 Training and Predicted Data for Machine Learning on Thermoelectric Materials Thomas, Andy; Chernyavskii, Dmitry This is supplementary information to the manuscript: "Sustainable Thermoelectric Materials Predicted by Machine Learning" 2022-01-01T00:00:00Z