Jan Schützke

Working on the automation of diffraction or spectroscopy data analysis. Instead of relying on manual preprocessing steps, figure-of-merit based matching or peak shape approximating procedures, let a neural network do the analysis.

While I started studing Mechanical Engineering at Karlsruhe Institute of Technology due to my interest in applied physics (rollercoasters, robots and simple machines that have often been realized using Legos), I primarily started using Python to automate certain tasks. Libraries such as pyspotify (RIP) gave me a reason to experiment with code-snippets in my free time… now I’m writing code full time.

After an internship focused on the organization of data from sensor manufacturing and attending machine learning classes, my career was set. I started as a student researcher at the Institute for Automation and Applied Informatics (IAI) and developed a proof-of-concept model for detection of synapses in microscopy images using the Tensorflow Object Detection API (discontinued). For my Master’s thesis, Bruker AXS GmbH had an interesting idea: Is it possible to detect mineral phases in X-ray powder diffraction scans from multi-compound samples using a neural network? Answers to this question and more can be found in the projects and publication pages on this site.

selected publications

npj Compu-Mats.

Validating neural networks for spectroscopic classification on a universal synthetic dataset

J. Schuetzke, N. J. Szymanski, and M. Reischl

npj Computational Materials, 2023

Bib HTML

@article{synthetic_benchmark,
  title = {Validating neural networks for spectroscopic classification on a universal synthetic dataset},
  author = {Schuetzke, J. and Szymanski, N. J. and Reischl, M.},
  journal = {npj Computational Materials},
  volume = {9},
  number = {1},
  pages = {100},
  year = {2023},
  doi = {10.1038/s41524-023-01055-y},
  publisher = {Nature Publishing Group UK London},
}

Adv. Intell. Syst.

Accelerating Materials Discovery: Automated Identification of Prospects from XRD Data in Fast Screening Experiments

J. Schuetzke, S. Schweidler, F. R. Münke, and 5 more authors

Advanced Intelligent Systems, 2024

Bib HTML

@article{schuetzke_2024,
  title = {Accelerating Materials Discovery: Automated Identification of Prospects from {XRD} Data in Fast Screening Experiments},
  author = {Schuetzke, J. and Schweidler, S. and M\"unke, F. R. and Orth, A. and Khandelwal, A. D. and Breitung, B. and Aghassi-Hagmann, J. and Reischl, M.},
  journal = {Advanced Intelligent Systems},
  volume = {6},
  number = {3},
  pages = {2300501},
  year = {2024},
  doi = {10.1002/aisy.202300501},
}