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Found 6 papers, 1 papers with code
On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials
In this letter, we present an extension to TensorNet, a state-of-the-art equivariant Cartesian tensor neural network potential, allowing it to handle charged molecules and spin states without architectural changes or increased costs.
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
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.
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning
We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2).
Chemical Physics Biological Physics Computational Physics
