58 citations · 79 across the 4 of their papers we have counts for
4 papers
Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Peter Eastman, Evan Pretti, Thomas E. Markland
The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model fo…
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera +1
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding mu…
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis +6
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents subs…
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…