Before You Click: Understanding the Potential Energy Surface of Water

Students and researchers pursuing molecular modeling or computational chemistry use readily available software such as Schrodinger, Gaussian, Arguslab, Hyperchem etc. to facilitate the visualization of molecules and calculation of their energy. A variety of computational tools form the basis of working of such software. The first step in using most of these software programs is to optimize the geometry of the input molecule. During such optimization procedure, the software searches for parameters of geometry such as bond length, bond angle and dihedral angle which results in the molecule’s minimum energy and hence the most stable geometry. While it is pertinent for the student to be able to choose the right computational tool to obtain reliable results, the visualization of potential energy diagram of the molecule is equally important. It may be appropriately said that the core of all the computational tools is rooted in a deep understanding of potential energy diagrams or potential energy surfaces (PESs). Potential energy surfaces are multidimensional graphs of potential energy against the various independent variables of geometrical parameters. They can span from three-dimensional representations (with two dimensions for the independent variables and one for energy) to more complex, higher-dimensional forms. A PES is often compared to a landscape, with hills, valleys, and ridges corresponding to high and low energy configurations. It is a challenge for undergraduate students to understand the PESs of polyatomic molecules as they have always dealt with potential energy diagrams that are of two dimensions only. This article discusses a simplified approach to grasp the concept of PES for polyatomic molecules, using water—the simplest polyatomic molecule—as an example. A three-dimensional PES graph is created in MS Excel using values calculated from the free molecular modeling software, ArgusLab. Additionally, the process of reducing the 3D plot to a 2D plot through slicing is also explained.