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.

• 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.

• 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.