Molecular dynamics is a computational simulation method that models the physical movement of atoms and molecules by numerically integrating Newton’s equations of motion under an interatomic force field. It produces time-resolved trajectories from which thermodynamic and kinetic properties are derived. The method bridges physics-based simulation and machine learning in computational biology and chemistry.
Overview
- Integrates Newtonian equations in small time steps to evolve atomic positions.
- Force fields approximate the potential energy governing interactions.
- Increasingly accelerated and surrogate-modelled with machine learning.
Mechanisms
- Numerical integrators advance positions and velocities stably.
- Force fields define bonded and non-bonded interactions.
- Thermostats and barostats enforce temperature and pressure ensembles.
- Trajectory analysis extracts conformational and kinetic insight.
Applications
- Protein folding and conformational sampling.
- Drug binding and free-energy estimation.
- Materials and biomolecular property prediction.