Molecular Dynamics (MD) Simulations using LAMMPS/GROMACS: Predicting Mechanical & Thermal Properties

To provide practical training in MD simulation workflows using LAMMPS, GROMACS, OVITO, PACKMOL, VMD, Avogadro, and Python for predicting mechanical and thermal properties of materials.

• Understand MD fundamentals, ensembles, boundary conditions, and force fields.
• Build and simulate CNTs, polymers, and metallic alloy systems.
• Run tensile deformation and thermal conductivity simulations.
• Analyze atomic trajectories using OVITO and visualization tools.
• Extract stress-strain curves, heat flux data, and material property values using Python.

🗓️ Day 1: Foundations, System Building & Nanomaterials (CNTs)

Core Concepts:

• Introduction to MD fundamentals: thermodynamic ensembles such as NVT, NPT, NVE, and periodic boundary conditions.
• Force field selection criteria: AIREBO for carbon networks, EAM for alloys, and OPLS-AA for polymers.
• Structure generation workflows: creating perfect and defective Carbon Nanotubes (CNTs) using free open-source tools.
• Deciphering the input script: anatomy of a molecular dynamics run script.

💻 Hands-on Lab (Google Colab)

• Task: Run a mini-tensile strain simulation on a Single-Walled Carbon Nanotube (SWCNT) using LAMMPS.
• Deliverable: Generate and export a .lammpstrj trajectory file ready for structural deformation rendering in OVITO.

🗓️ Day 2: Polymers & Thermal Properties (GROMACS/LAMMPS)

Core Concepts:

• Amorphous structures: using PACKMOL to density-pack and build disordered polymer chains.
• Relaxation protocols: equilibrating high-density polymer matrices without system overlaps or explosive crashes.
• Thermal transport theory: understanding Green-Kubo relations versus Non-Equilibrium MD (NEMD) approaches.
• Calculating thermal conductivity (κ) across interfaces and bulk materials.

💻 Hands-on Lab (Google Colab)

• Task: Execute a thermal dissipation workflow to calculate the thermal conductivity of a material matrix.
• Deliverable: Write a Python parser to plot the heat flux autocorrelation function and extract the numerical thermal conductivity coefficient.

🗓️ Day 3: Alloys, Mechanical Testing & Publication Analysis

Core Concepts:

• Metallic alloys: implementing Embedded Atom Method (EAM) potentials for multi-element metal systems.
• Defect tracking: simulating grain boundaries, vacancies, and dislocation loops.
• Virtual mechanical testing: setting up algorithms for uniaxial tensile, compression, and shear testing.
• Data extraction: translating raw atomic data into engineering metrics such as Young’s Modulus, Yield Strength, and Ultimate Tensile Strength.

💻 Hands-on Lab (Google Colab)

• Task: Simulate a dynamic stress-strain deformation test on a metallic alloy crystal lattice.
• Deliverable: Auto-generate a publication-ready stress-strain curve directly from the raw simulation data logs using Python.

• Students from materials science, nanotechnology, mechanical engineering, chemical engineering, polymer science, and related fields.
• PhD scholars and researchers working in computational materials science, molecular simulations, nanomaterials, polymers, or alloys.
• Academicians and faculty members interested in MD simulation-based teaching and research.
• Industry professionals working in materials design, R&D, simulation, manufacturing, thermal analysis, or mechanical testing.
• Learners interested in using LAMMPS, GROMACS, OVITO, PACKMOL, VMD, Avogadro, and Python for material property prediction.
• Set up basic molecular dynamics simulation workflows.
• Generate CNT, polymer, and alloy material structures.
• Run LAMMPS/GROMACS simulations for mechanical and thermal studies.
• Visualize atomic deformation, defects, and structural changes using OVITO.
• Calculate thermal conductivity from simulation data.
• Extract stress-strain curves and mechanical property values.
• Create publication-ready plots using Python.

• Set up basic molecular dynamics simulation workflows.
• Generate CNT, polymer, and alloy material structures.
• Run LAMMPS/GROMACS simulations for mechanical and thermal studies.
• Visualize atomic deformation, defects, and structural changes using OVITO.
• Calculate thermal conductivity from simulation data.
• Extract stress-strain curves and mechanical property values.
• Create publication-ready plots using Python.