Introducing Students to Scientific Computing in the Laboratory through Python and Jupyter Notebooks
Charles J. Weiss, A. Klose · ACS symposium series · 2021
Described in this chapter are three undergraduate laboratory activities designed to introduce undergraduate chemistry students to scientific computing through Python and Jupyter notebooks. The activities include simulating first-order radioactive decay kinetics using random number generators in a General Chemistry course, having students calculate the entropy of a substance using specific heat capacity and enthalpy data in a first-year or intermediate-level course, and performing non-linear curve fitting of real gas data in an advanced-level physical chemistry course. None of these activities assume students have previous computer programming experience and all walk the students through simulations, loading, processing, analyzing, and visualizing data. To compensate for the lack of student programming experience, the activities leverage the Jupyter notebook code and markdown cell structure to provide additional prompting and explanation and sometimes include prewritten code. Each activity incorporates computing through a different model ranging from students simply modifying and running code to writing their own Python code. This chapter will discuss not only the activities, but lessons learned from teaching the activities and challenges faced in incorporating computing into the undergraduate chemistry curriculum.