Browsing by Author "Thomas, Andy"

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  • ItemOpen Access
    Jupyter notebook code and example file for the evaluation of XRR data via FFT
    (Technische Universität Dresden, 2020-11-14) Lammel, Michaela; Thomas, Andy
    Fast Fourier transform and multi-Gaussian fitting of XRR data to determine the thickness of ALD grown thin films within the initial growth regime (FFT_XRR_ALD). Jupyter notebook for the evaluation of XRR data by utilizing fast Fourier transformation and a multi-Gaussian fitting routine for the determination of ultra thin ALD films within the initial growth regime. One example measurement is included.
  • ItemOpen Access
    Lorentz transmission electron microscopy and Hall effect data of Mn1.4PtSn
    (Technische Universität Dresden, 2024-09-21) Thomas, Andy; Pohl, Darius
    The extended dataset of our corresponding publications: 1) Images_Field_Sweep: Lorentz TEM images of the field sweep 2) Images_Sample_Tilt: Lorentz TEM images of the tilt series 3) MnPtSn_Pos7_Loop_0_500_-500_0mT_25uA_2sec.ma7: ASCII data magnetotransport of the field sweep in (1) 4) MnPtSn_Pos7_AS_Tilt_-7_Start_delta_50uA.ma7: ASCII data magnetotransport of the tilt series in (2) 5) Video_Field_Sweep.avi: Lower resolution video of all images in (1) 6) Video_Sample_Tilt.avi: Lower resolution video of all images in (2)
  • ItemOpen Access
    Python source code, text corpora and automatically generated datasets for Machine Learning
    (Technische Universität Dresden, 2023-07-12) 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"
  • ItemOpen Access
    Supplemental material to "Field-induced condensation of π to 2π soliton lattices in chiral magnets"
    (Technische Universität Dresden, 2026-06-30) Winter, Moritz; Pignedoli, Alessandro; Rahn, Marein; Sukhanov, Aleksandr; Achinuq, Barat; Bollard, Jack; Azhar, Maria; Everschor-Sitte, Karin; Pohl, Darius; Schneider, Sebastian; Tahn, Alexander; Victor, Ukleev; Valvidares, Manuel; Thomas, Andy; Wolf, Daniel; Vir, Praveen; Helm, Toni; van der Laan, Gerrit; Hesjedal, Thorsten; Geck, Jochen; Rellinghaus, Bernd
    This dataset contains the experimental and simulation data underlying the figures of the corresponding publication, which reports a field-induced crossover from π to 2π chiral soliton lattices in the Heusler compound Mn1.4PtSn. All measurements were performed on the same focused-ion-beam lamella at room temperature, with the magnetic field applied out of plane (along the crystallographic c axis). The data are organized by method: (1) 1_LTEM — Lorentz transmission electron microscopy: transport-of-intensity (TIE) reconstructions of the magnetic phase and in-plane induction on a region of interest as a function of field (Figs 2, 3, 6), the two full-field raw micrographs at 0 and 300 mT shown in Fig 1, and the measured stripe period and domain widths vs field (Fig 3); (2) 2_REXS — resonant elastic X-ray scattering at the Mn L3 edge (Diamond I10): raw detector frames of the 0–550 mT field upsweep (Fig 1) and the extracted propagation-vector magnitude and harmonic intensities vs field (Figs 3, 4), with the analysis notebook; (3) 3_micromagnetics — Mumax3 input scripts and output (magnetization fields, energies, applied field) for the final field sweep that reproduces the measured modulation length, domain widths and harmonic content (Figs 6, 7, 8); (4) 4_model — a phenomenological 1D stripe-domain model (script, generated spin textures and induction profiles) used to deduce and visualize the spin textures (Figs 2g–o, 6); (5) figure_plots — the OriginPro projects holding the plotted data behind Figs 3, 4, 6 and 8. Together these data establish that the magnetic ground state of Mn1.4PtSn is a π-soliton lattice rather than a helical spiral, and that it condenses continuously into a conventional 2π-CSL under increasing out-of-plane field. A top-level README and Fig_to_data_map.csv document the file formats and the mapping between data and figures.
  • ItemOpen Access
    Training and Predicted Data for Machine Learning on Thermoelectric Materials
    (Technische Universität Dresden, 2022-04-07) Thomas, Andy; Chernyavskii, Dmitry
    This is supplementary information to the manuscript: "Sustainable Thermoelectric Materials Predicted by Machine Learning"