Students have difficulty understanding the abstract atomic world of chemistry. They often learn concepts by an algorithmic approach rather than by truly understanding the underlying theory. This problem is in part due to the three-dimensional, dynamic nature of chemistry, as well as the mathematical models used to predict chemical behavior. Computational chemistry has the power to overcome these obstacles to student learning. It allows students to perceive molecules and electronic structure in three dimensions. It displays dynamical information critical to chemical behavior. Students can manipulate mathematical parameters and observe the atomic consequences. To aid student learning, this project incorporates computational chemistry into our physical, organic, and biochemistry curricula. Computational chemistry is increasingly applied at the workplace in all fields of chemistry and biochemistry. By experiencing a computational curriculum closely tied to experiments, students learn to appreciate computational chemistry in the context in which it is most often used, to predict and interpret experimental data. Free energy simulations are used to study the thermodynamics of protein folding, ligand binding, and solvation effects and of other large systems. These methods, not yet exploited in the undergraduate curriculum, have great pedagogical value in giving concrete examples of the molecular basis for thermodynamics. Since they depend on classical Newtonian physics, the underlying theory is very accessible to the undergraduate student. Through this project, undergraduate laboratories are developed using molecular-dynamics-based free energy simulations. The laboratories developed, tested, and refined here on graphics workstations are easily performed on the next generation of PCs. *
