Molecular simulations, which simulate the motions of particles according to fundamental
laws of physics, have been applied to a wide range of fields from physics and materials …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Multiscale modeling has a long history of use in structural biology, as computational
biologists strive to overcome the time-and length-scale limits of atomistic molecular …

The development of reliable and extensible molecular mechanics (MM) force fields—fast,
empirical models characterizing the potential energy surface of molecular systems—is …

Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest
due to their capacity to span spatiotemporal scales of classical interatomic potentials at …

Implicit solvent force fields are computationally efficient but can be unsuitable for running
molecular dynamics on disordered proteins. Here I improve the a99SB-disp force field and …

An accurate, generalizable, and transferable force field plays a crucial role in the molecular
dynamics simulations of organic polymers and biomolecules. Conventional empirical force …

Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in
drug discovery. Molecular mechanics/Generalized-Born (Poisson–Boltzmann) surface area …

Force fields are a key component of physics-based molecular modeling, describing the
energies and forces in a molecular system as a function of the positions of the atoms and …

Accurately describing long-range interactions is a significant challenge in molecular
dynamics (MD) simulations of proteins. High-quality long-range potential is also an …