20 citations · 25 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
Paul Fuchs, Michał Sanocki, Julija Zavadlav
Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model i…
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs, Stephan Thaler, Sebastien Röcken +1
Neural Networks (NNs) are effective models for refining the accuracy of molecular dynamics, opening up new fields of application. Typically trained bottom-up, atomistic NN potentia…