Artificial Intelligence Meets Molecular Dynamics
We develop a physics-informed machine learning framework for the reverse transformation of coarse-grained macromolecules to atomistic detail. By using modern machine learning techniques and incorporating physical and chemical knowledge, the backmapping process will be accelerated and the accuracy will be increased compared to state-of-the-art algorithms. The final python package will be made available to the community following the FAIR (Fair, Accessible, Interoperable, and Reusable) principles.
- 2021 Master of Science in Bioinformatics, University of Saarland in Saarbruecken
- 2020-2021 Student assistant at Chair for Clinical Bioinformatics, University of Saarland in Saarbruecken
- 2017 Student assistant in Department Computational Biology, Friedrich-Alexander University in Erlangen
- 2016 Bachelor of Science in Biomathematics/Biophysics, Friedrich-Alexander University in Erlangen