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Technical University of Munich

Coarse-Graining

Reaching longer time-scales with coarse-grained molecular dynamics.
Recent Papers:
Mapping Still Matters: Coarse-Graining with Machine Learning Potentials

F. Görlich, J. Zavadlav, Preprint 2025, arXiv, GitHub

Investigating how the coarse-grained mapping influences the learned representation by modern, equivariant machine learning potentials. 

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials

W. Chen, F. Görlich, P. Fuchs, J. Zavadlav, J. Chem. Theory Comput. 2025, paper, GitHub

Enhanced sampling with unbiased force recomputation improves force-matched coarse-grained machine-learning potentials.

Predicting solvation free energies with an implicit solvent machine learning potential

S. Röcken, A. F. Burnet, J. Zavadlav, J. Chem. Phys. 2024, paper , arXiv, GitHub

 

Implicit solvent ML potentials can outperform explicit solvent atomistic models in both accuracy and efficiency.

Deep Coarse-grained Potentials via Relative Entropy Minimization

S. Thaler, M. Stupp, J. Zavadlav, J. Chem. Phys. 2022, paper, arXiv, GitHub

Coarse-grained ML potentials trained with Relative Entropy result in more accurate potential energy surfaces, require less data and can be employed with larger integration time steps compared to the conventional training with Force Matching.

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