Monte Carlo NVT Simulations for Chemical Engineering

Understanding how molecules interact and behave at the microscopic level is essential for modern chemical engineering, materials design, and physics. This course provides a clear, conceptual pathway to understanding and writing Monte Carlo simulations in the canonical (NVT) ensemble. You will learn to translate abstract statistical mechanics into working algorithms, building a solid foundation in molecular modeling. What you'll learn: Understand the foundational concepts of statistical thermodynamics and the NVT ensemble; Master the Metropolis Monte Carlo algorithm and its role in sampling configuration space; Apply periodic boundary conditions and the minimum image convention to avoid edge effects; Implement simulation steps, particle displacements, and energy evaluations using clean Python code; Analyze simulation outputs to calculate thermodynamic properties like potential energy and pressure; Practice debugging and validating your simulation code against known physical behaviors. This course begins with core terminology and theoretical definitions before guiding you through the step-by-step construction of a simulation. It is ideal for undergraduate students, researchers, and engineers looking for a clear, accessible entry point into computational molecular science. No advanced background in statistical mechanics is required.