3 papers
physics.chem-ph2024
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…
physics.chem-ph2024
Predicting solvation free energies with an implicit solvent machine learning potential
Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations…
physics.chem-ph2023
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken, Julija Zavadlav
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-…