activity
20102024
most citedEfficient stochastic thermostatting of path integral molecular dynamics

433 citations · 842 across the 11 of their papers we have counts for

collaborators
Showing physics.chem-phShow all

9 papers · 1 filter

physics.chem-ph20243 cited

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning

Frank Hu, Michael S. Chen, Grant M. Rotskoff +2

Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR sp…

physics.chem-ph20241 cited

Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…

physics.chem-ph202321 cited

Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations

Austin O. Atsango, Tobias Morawietz, Ondrej Marsalek +1

The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanism…

physics.chem-ph20233 cited

Elucidating the role of hydrogen bonding in the optical spectroscopy of the solvated green fluorescent protein chromophore: using machine learning to establish the importance of high-level electronic structure

Michael S. Chen, Yuezhi Mao, Andrew Snider +5

Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which a…

physics.chem-ph202213 cited

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Peter Eastman, Pavan Kumar Behara, David L. Dotson +9

Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe…

physics.chem-ph202212 cited

Simulating nuclear and electronic quantum effects in enzymes

Lu Wang, Christine M. Isborn, Thomas E. Markland

An accurate treatment of the structures and dynamics that lead to enhanced chemical reactivity in enzymes requires explicit treatment of both electronic and nuclear quantum effects…