48 citations · 68 across the 2 of their papers we have counts for
2 papers
physics.chem-ph2023★ 20 cited
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
physics.chem-ph2023★ 48 cited
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara +11
The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is in…