3 papers
physics.chem-ph2025
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Weilong Chen, Franz Görlich, Paul Fuchs +1
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning poten…
physics.comp-ph2025
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
Paul Fuchs, Weilong Chen, Stephan Thaler +1
Machine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to…
physics.chem-ph2024
Thermodynamic Interpolation: A generative approach to molecular thermodynamics and kinetics
Selma Moqvist, Weilong Chen, Mathias Schreiner +2
Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In t…