Molecular Dynamics Water Study

This project is an investigation of water properties using molecular dynamics simulations with the TIP4P water model at different temperatures. It provides a comprehensive analysis system for comparing water properties across different conditions.

Project Overview

The MD Water Study project aims to:

1. Analyze water properties at different temperatures (273K, 298K)
2. Provide reproducible workflows for water simulations
3. Generate quality analyses and visualizations
4. Establish temperature-dependent water properties

Project Structure

The project consists of:

1. tip4p_273K/ - TIP4P water model at 273K
2. tip4p_298K/ - TIP4P water model at 298K
3. analysis/ - Analysis scripts and results
4. visualization_scripts/ - Visualization tools
5. visualization_outputs/ - Generated visualizations
6. comparison/ - Comparative analysis results

Each simulation directory follows a consistent structure:

simulation_directory/
├── analysis/                # Analysis results
├── scripts/                 # Analysis scripts
├── md.mdp                   # Production MD parameters
├── nvt.mdp                  # NVT equilibration parameters
├── em.mdp                   # Energy minimization parameters
├── topol.top               # System topology
├── box_5500.gro            # Initial system coordinates
├── md.gro                   # Final system coordinates
├── md.xtc                   # Production trajectory
├── md.trr                   # Production trajectory (full precision)
├── md.edr                   # Energy data
├── md.log                   # MD log file
└── various checkpoint files

Water Model Details

TIP4P Model

• Description: 4-site model with negative charge on dummy atom (M-site)
• Key Parameters:
  • Oxygen sigma: 0.31644 nm
  • Oxygen epsilon: 0.7749 kJ/mol
  • Hydrogen charges: +0.52e
  • M-site charge: -1.04e

Simulation Methodology

All simulations follow a consistent protocol:

1. System Creation:

   • Generate a water box with 5500 water molecules
   • Create topology with TIP4P water model parameters

2. Energy Minimization:

   • Steepest descent algorithm to remove bad contacts
   • Careful minimization with small step size

3. Equilibration:

   • NVT equilibration to stabilize temperature
   • NPT equilibration to stabilize pressure (1 bar) and density

4. Production:

   • Production run with 1-2 fs timestep
   • Periodic boundary conditions
   • PME electrostatics with 1.0 nm cutoff
   • V-rescale thermostat and Parrinello-Rahman barostat

Analysis Capabilities

The project includes comprehensive analysis tools:

1. Structural Analysis:

   • RMSD analysis and drift visualization
   • Radial distribution functions
   • Coordination numbers

2. Dynamic Properties:

   • Mean square displacement
   • Diffusion coefficients
   • Velocity autocorrelation functions

3. Thermodynamic Properties:

   • Temperature and pressure profiles
   • Energy components
   • Density calculations

4. Comparative Analysis:

   • Temperature-dependent comparisons
   • Pressure analysis
   • Energy comparisons
   • RMSD comparisons

Visualization Tools

The project includes advanced visualization capabilities:

1. Trajectory Visualization:

   • RMSD drift visualization
   • Combined RMSD plots
   • Temperature and pressure profiles

2. Analysis Visualization:

   • Energy component plots
   • Structural property plots
   • Dynamic property plots

Dependencies

• GROMACS: For simulation and analysis
• Python 3 with:
  • NumPy
  • Matplotlib
  • SciPy
  • Seaborn

Usage

Running Simulations

cd tip4p_273K  # or tip4p_298K
gmx mdrun -v -deffnm md

Running Analysis

python analysis/rmsd_drift_visual.py
python analysis/energy_comparison.py
python analysis/temperature_comparison.py
python analysis/pressure_comparison.py

Generating Visualizations

python visualization_scripts/plot_combined_rmsd.py
python visualization_scripts/rmsd_comparison.py

Future Work

Planned extensions to this project include:

1. Additional temperature points
2. Pressure dependence studies
3. Interface and solvation studies
4. Machine learning analysis of water structure and dynamics