Monte Carlo Simulation and the Metropolis Algorithm for Chemical Engineers

Chemical engineering problems often involve complex, high-dimensional molecular systems that are impossible to solve analytically. To predict thermodynamic properties and understand molecular behavior, engineers rely on powerful stochastic simulation tools. This text-based course guides you from the fundamental principles of statistical mechanics to the practical implementation of the Metropolis Monte Carlo algorithm. You will learn how to set up, run, and analyze molecular simulations specifically tailored for chemical engineering applications, including modern practices in data processing and code optimization. What you'll learn: Understand the statistical mechanics foundation behind stochastic molecular modeling; Implement the Metropolis Monte Carlo algorithm from scratch using clean, modern programming practices; Simulate phase equilibria and molecular configurations for chemical systems; Analyze simulation output to calculate thermodynamic properties like energy and pressure; Apply error analysis and convergence checks to ensure simulation reliability. The course begins with essential definitions, thermodynamic ensembles, and the mathematical theory of Markov chains, before guiding you step-by-step through writing and running your own simulation scripts. This course is designed for undergraduate students, researchers, and practicing chemical engineers who are new to molecular simulation and want a clear, structured introduction to numerical methods. Start building your computational engineering skillset today.