162 citations · 259 across the 10 of their papers we have counts for
11 papers
Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
Felix Pultar, Moritz Thuerlemann, Igor Gordiy +2
We present design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybri…
Hybrid Classical/Machine-Learning Force Fields for the Accurate Description of Molecular Condensed-Phase Systems
Moritz Thürlemann, Sereina Riniker
Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are…
DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment
Marc T. Lehner, Paul Katzberger, Niels Maeder +4
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attentio…
Energy-Based Clustering: Fast and Robust Clustering of Data with Known Likelihood Functions
Moritz Thürlemann, Sereina Riniker
Clustering has become an indispensable tool in the presence of increasingly large and complex data sets. Most clustering algorithms depend, either explicitly or implicitly, on the…
Implicit Solvent Approach Based on Generalised Born and Transferable Graph Neural Networks for Molecular Dynamics Simulations
Paul Katzberger, Sereina Riniker
Molecular dynamics (MD) simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the en…
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…
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…
RE-EDS Using GAFF Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules
Salome R. Rieder, Benjamin Ries, Kay Schaller +4
Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic int…
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…
Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems
Lennard Böselt, Moritz Thürlemann, Sereina Riniker
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the…
Volume-Scaled Common Nearest Neighbor Clustering Algorithm with Free-Energy Hierarchy
R. Gregor Weiß, Benjamin Ries, Shuzhe Wang +1
The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of mol…