162 citations · 182 across the 3 of their papers we have counts for
3 papers
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…
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…