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Technische Universität München

Coarse-Graining

Reaching longer time-scales with coarse-grained molecular dynamics.
Recent Papers:
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials

Chen, Weilong; Görlich, Franz; Fuchs, Paul; Zavadlav, Julija, Preprint 2025, arXiv, GitHub

 

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 acurate potential energy srufaces, require less data and can be employed with larger integration timesteps compared to the conventional training with Force Matching.

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