Most computational chemistry instructional activities insulate students from the computer code, as students typically complete the exercise by interacting with the software through some sort of graphical user interface (GUI). Although the use of GUIs is advantageous in some applications, the disadvantage of GUI-based activities is that the students' user experience (UX) is substantially removed from how work in computational chemistry is actually done (we write code). Motivated by a desire to create instructional activities that give students a more realistic coding-based experience in computational chemistry, a pair of instructional activities were created around a simplified molecular dynamics simulation (a Lennard-Jones fluid), which was written in a highly readable programming language (Python). The first activity, currently used in an undergraduate physical chemistry lab course, is titled “Lennard-Jones Fluid Simulation Lab,” and involves students learning to write some very basic Python code in a Jupyter notebook that is used to control a lightweight Lennard-Jones fluid simulation (runs on any laptop), and which culminates in fitting a Van der Waals gas model to simulation data produced by the LJ fluid simulation code. The LJ lab activity also includes a roughly 2-hour YouTube video where the author demonstrates how to complete the activity, enabling instructors to assign this lab regardless of their coding experience. The second instructional activity based around the LJ fluid simulation code is a jigsaw type activity titled “LJ fluid simulation code dissection” (akin to dissections in biology courses, except students take code apart to see how it works instead of an animal). This activity, recently implemented in the author’s graduate course, “Introduction to Computational Chemistry,” is centered around students being assigned different portions from the LJ fluid simulation code, on which they must both give an oral presentation and write a Jupyter notebook that demonstrates how their portion of the code works. The activity results in students developing a deeper understanding of how molecular simulations work, as well as valuable experience in presenting computational chemistry methodologies via two commonly used modalities. Both the simulation code and the Jupyter notebook containing the guided activity for undergraduate physical chemistry students are available as a free download from the author’s GitHub page.

High-transparency computational chemistry instructional exercises based around Lennard-Jones fluid simulations coded in python

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