3 papers
physics.chem-ph2022
Graph Convolutional Neural Networks for (QM)ML/MM Molecular Dynamics Simulations
Albert Hofstetter, Lennard Böselt, Sereina Riniker
To accurately study chemical reactions in the condensed phase or within enzymes, both a quantum-mechanical description and sufficient configurational sampling is required to reach…
physics.chem-ph2022
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions
Moritz Thürlemann, Lennard Böselt, Sereina Riniker
Simulations with an explicit description of intermolecular forces using electronic structure methods are still not feasible for many systems of interest. As a result, empirical met…
physics.chem-ph2021
Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks
Moritz Thürlemann, Lennard Böselt, Sereina Riniker
The accurate description of electrostatic interactions remains a challenging problem for fitted potential-energy functions. The commonly used fixed partial-charge approximation fai…