md-analysis

MD Trajectory Analysis

Description

This skill covers quantitative post-processing of atomistic molecular dynamics trajectories: loading and preprocessing trajectories from LAMMPS, GROMACS, or ASE; computing structural observables (RDF, coordination numbers, angular distributions, bond orientational order, common-neighbor analysis, centrosymmetry parameter); computing dynamical observables (MSD, diffusion coefficient, velocity autocorrelation, residence time, relaxation times); extracting thermodynamic observables from simulation logs (temperature, pressure, energy drift, stress, density); analyzing interfaces and defects; and applying statistical methods (block averaging, autocorrelation, equilibration trimming) to obtain reliable estimates with uncertainty bounds. Covers MDAnalysis, OVITO, ASE, pandas, and matplotlib. Invoke this skill after a production MD run is complete, or during a run to compute rolling diagnostics.

Domain Context

A molecular dynamics trajectory is a time-ordered sequence of configurations — positions, and optionally velocities and forces — generated by integrating Newton's equations of motion. Post-processing transforms this sequence into physically interpretable observables. Several subtleties govern what can and cannot be concluded from trajectory analysis:

• Ensemble vs. time average: MD computes time averages, which equal ensemble averages only if the system is ergodic on the simulation timescale. For solid-state systems with slow structural relaxation, diffusion-limited processes, or configurations separated by large barriers, the time average may not have converged to the ensemble average within the simulation length. Quoting an observable from a non-ergodic trajectory as an equilibrium property is a fundamental error.
• Equilibration contamination: Frames collected during equilibration do not belong to the target ensemble. They must be excluded from all analysis averages. The equilibration period is not fixed by the simulation protocol; it must be verified from the data (see md-equilibration). Including even 10–20% of a typical equilibration phase in a production average can shift observables by several percent.
• Periodic boundary conditions and unwrapping: MD codes write wrapped coordinates — atom positions are mapped into the primary simulation box. This is correct for most structural observables but corrupts center-of-mass–based quantities such as MSD and diffusion coefficient. Unwrapping must be applied before any displacement-based analysis.
• Statistical uncertainty: A single MD trajectory is a single realization of a stochastic process. Successive frames are correlated over a timescale τ (the autocorrelation time of the observable). Treating N correlated frames as N independent measurements underestimates the statistical error by √(N/N_eff) where N_eff = N / (2τ/dt). Block averaging and autocorrelation analysis are required for honest error estimates.
• Topology vs. trajectory: Many analysis codes (MDAnalysis, GROMACS) separate the static topology (atom types, masses, bond connectivity) from the time-varying trajectory (coordinates, velocities). The topology must be consistent with the trajectory — mismatched atom counts, types, or connectivity produce silently wrong results rather than informative errors.
• Force-field vs. MLP trajectories: Classical force-field MD and MLP-driven MD produce trajectories in identical formats, but MLP trajectories may contain configurations with elevated committee uncertainty (extrapolation). Uncertainty-aware post-processing — tagging frames by committee force std — is required before reporting thermodynamic averages from MLP trajectories when the training set coverage is not established.