About Me

Hi!

I am Kai Riedmiller. By training, I am a theoretical chemist with a focus on machine learning and data science.

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Education

PhD engineering sciences, University Heidelberg

Title: Predicting Hydrogen Atom Transfer in Collagen
I worked on creating a machine learned model for predicting chemical reactivity based on the 3D structure of a molecular system.

Master of Science, Chemistry, University Konstanz

Title: Machine learning methods for characterization of mineralization processes
Classification of small molecular clusters forming crystallization seeds using autoencoders.

I work as a PostDoc at the Heidelberg Institute for Theoretical Studies, HITS, in Heidelberg in the Molecular Biomechanics Group of Prof. Frauke Gräter.

Tools

Tools I learned during my studies, or by myself:

• Python, for
  • Workflow automation
  • Machine learning
  • Data visualization
  • Data analysis
  • Publishing software
• Tensorflow/Pytorch
• Gromacs
  • Molecular Dynamics engine
• docker
• git, github, github actions
• Blender
  • 3D Visualizations
  • for molecules and data just as much as for more artistic projects
• Inkscape
• LaTeX
• Linux, bash

My Publications

• Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces:
  • Molecular Dynamics in the TEA Challenge 2023
  • Model Analysis in the TEA Challenge 2023
  • Poltavsky, I.; Puleva, M.; Charkin-Gorbulin, A.; Fonseca, G. C.; Batatia, I.; Browning, N. J.; Müller, C.; Riedmiller, K.; Rupp, M.; Csanyi, G.; von Lilienfeld, O. A.; Müller, K.R.; Tkatchenko, A.; et al., preprint 2024
  • Part 1: MD
  • Part 2: Models
  • 

• Predicting Hydrogen Atom Transfer in Collagen
  • Riedmiller, K; Dissertation, 2024
  • 
  • https://doi.org/10.11588/heidok.00035153
• Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins
  • Riedmiller, K.; Reiser, P.; Bobkova, E.; Maltsev, K.; Gryn’ova, G.; Friederich, P.; Gräter, F. Chem. Sci. 2024.
  • Chemical Science
  • 
• Scalable stellar evolution forecasting
  • Maltsev, K.; Schneider, F. R. N.; Röpke, F. K.; Jordan, A. I.; Qadir, G. A.; Kerzendorf, W. E.; Riedmiller, K.; Smagt, P. van der. A&A 2024, 681, A86.
  • Astronomy & Astrophysics
• Collagen breaks at weak sacrificial bonds taming its mechanoradicals
  • Rennekamp, B.; Karfusehr, C.; Kurth, M.; Ünal, A.; Monego, D.; Riedmiller, K.; Gryn’ova, G.; Hudson, D. M.; Gräter, F. Nat Commun 2023, 14 (1), 2075.
  • Nature Communications
  • 
• DOPA Residues Endow Collagen with Radical Scavenging Capacity
  • Kurth, M.; Barayeu, U.; Gharibi, H.; Kuzhelev, A.; Riedmiller, K.; Zilke, J.; Noack, K.; Denysenkov, V.; Kappl, R.; Prisner, T. F.; Zubarev, R. A.; Dick, T. P.; Gräter, F. Angewandte Chemie International Edition 2023, 62 (24), e202216610.
  • Angewandte Chemie International Edition
  • 
• Bond dissociation energies of X–H bonds in proteins
  • Treyde, W.; Riedmiller, K.; Gräter, F. RSC Advances 2022, 12 (53), 34557–34564.
  • RSC Advances
  • 
• Guanidine-II aptamer conformations and ligand binding modes through the lens of molecular simulation
  • Steuer, J.; Kukharenko, O.; Riedmiller, K.; Hartig, J. S.; Peter, C. Nucleic Acids Research 2021, 49 (14), 7954–7965.
  • Nucleic Acids Research
  • 
• Remote Perfluoroalkyl Substituents are Key to Living Aqueous Ethylene Polymerization
  • Schnitte, M.; Scholliers, J. S.; Riedmiller, K.; Mecking, S. Angewandte Chemie International Edition 2020, 59 (8), 3258–3263.
  • Angewandte Chemie International Edition
  • 

Link to original