Physical chemistry describes chemical phenomena in the language of mathematics at a level of conceptual and theoretical rigor beyond that which many undergraduate chemistry students are typically unaccustomed. This means that for students of physical chemistry, they must develop a wide variety of mathematical skills "on the job," while leaving enough intellectual bandwidth to learn the physical chemistry concepts themselves. For many students this is an enormously challenging proposition, which contributes to physical chemistry's reputation as one of (if not the) most difficult courses taught on any college campus. While mathematical rigor is the cost of doing business in physical chemistry, being able to perform every requisite mathematical transformation by hand does not have to be a learning objective for the course. To make the study of physical chemistry more mathematically accessible for undergraduate chemistry students, the goal of this Module is to help students develop the cyberinfrastructure (CI) skills necessary to use Python as a symbolic and numerical mathematics engine for solving physical chemistry problems.
This Module is broken into lessons focusing on helping students develop the essential CI skills for using Python as a mathematics engine in physical chemistry contexts, rather than deeply discussing the mathematics itself.
| Lesson # | Directory | Title | Time |
|---|---|---|---|
| 1 | 1_symbolic-algebra |
Introduction to Symbolic Algebra in Python | 1-2 hrs |
| 2 | 2_symbolic-calculus |
Introduction to Symbolic Calculus in Python | 2-3 hrs |