Fields of Research
Machine learning of structure-property-relationships
- Patterns in chemical sequences that control, for instance, the interaction of copolymers with lipid membranes.
- Hidden physical variables in chemical space revealed by „trans-encoder“ neural networks .
- Inverse problems and artificial intelligence in soft materials design.
Static and dynamic conformation patterns
- Unsupervised learning of classes of polymer conformations and collective ordering.
- Precursor patterns for crystal nucleation in entangled polymer melts.
Data-driven coarse-graining of simulation models
- Machine learned implicit solvents and their potential for larger scale simulations of self-assembly.
- Transfer learning of physical patterns between different levels of coarse-graining.
 M. Werner, ACS Macro Letters 10 (2021) 1333-1338.
 M. Werner, Y. Guo, V.A. Baulin, npj Computational Materials 6 (2020) 72.