3 papers
physics.chem-ph2025
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-ph2025
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…
cs.LG2025
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
Sebastien Röcken, Julija Zavadlav
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availabi…