Integrating Python into a Physical Chemistry Lab

Abstract

This paper shows a method for integrating computer programming into a standard physical chemistry laboratory sequence to augment student data analysis abilities and allow them to carry programming skills forward to other courses. The Python programming language is used, taking advantage of the pedagogical benefits of Jupyter notebooks, primarily the ability to intersperse instructions and interactive code cells. A series of five notebooks, plus one traditional script exercise, are designed to teach basic techniques (e.g., loops, assignments, data types), instrument interfacing, model fitting, and introductory quantum chemistry. The skills are directly applicable to laboratories the students perform during the hands-on portion of the courses. A survey of students who have completed the course show high confidence in their ability to learn new skills, and student comments reveal they have used these skills in a variety of other contexts.

eChem: A notebook exploration of quantum chemistry

“…3,8,9 The educational benefits of interactive notebooks have been realized in many fields, with examples in, e.g., bioscience and informatics, 10 and radiology physics. 11 In chemistry, specifically, notebooks have been developed on the topics of scientific computing, 12,13 analytical chemistry, 14 stochastic simulations of processes, 15 labs in physical chemistry, 16 machine learning, 17,18 molecular docking, 19 or to explain basic concepts like the hydrogen molecule, 20 the particle in a box, 21 reciprocal space, 22 and more. It has been noted that deeper insights are gained after using interactive notebooks, 21 and students have been seen to quickly adopt Jupyter notebooks also for other courses.…”

eChem: A notebook exploration of quantum chemistry

“…3,8,9 The educational benefits of interactive notebooks have been realized in several fields with examples in, e.g., bioscience and informatics, 10 and radiology physics. 11 In chemistry, specifically, notebooks have been developed on the topics of scientific computing, 12,13 analytical chemistry, 14 stochastic simulations of processes, 15 labs in physical chemistry, 16 machine learning, 17,18 molecular docking, 19 or to explain basic concepts like the hydrogen molecule, 20 the particle in a box, 21 reciprocal space, 22 and more. It has been noted that deeper insights are gained after using interactive notebooks, 21 and students have been seen to quickly adopt Jupyter notebooks also for other courses.…”

Using Jupyter Tools to Design an Interactive Textbook to Guide Undergraduate Research in Materials Informatics

“…In particular, when designing any informatics curriculum, it is appealing to include programming exercises to give students hands-on experience with authentic problem solving tasks . Given the popularity of the beginner-friendly Python programming language in scientific research, many introductory-level programming courses in the physical sciences have chosen to use Python as well. − To improve code readability and reproducibility, there is a growing trend to write and execute Python code inside Jupyter notebooks, which are now commonplace in chemoinformatics/MI courses ,, and workshops. − These digital notebooks merge prose, Python code, and additional multimedia elements into rich computational narratives that provide a gentle introduction for students. They are often provided as standalone files ( extension) for users to run locally, but this requires installing additional Python packages and managing software configurations on personal devices, which can be daunting for beginners.…”