https://github.com/ghutchis/chem1000

Jupyter Noteboks for lecture and assignments for CHEM 1000: Mathematics for Chemistry at University of Pittsburgh
https://github.com/ghutchis/chem1000

Last synced: 30 days ago
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Jupyter Noteboks for lecture and assignments for CHEM 1000: Mathematics for Chemistry at University of Pittsburgh

• Host: GitHub
• URL: https://github.com/ghutchis/chem1000
• Owner: ghutchis
• License: other
• Created: 2020-06-19T19:46:09.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-01-10T18:59:21.000Z (10 months ago)
• Last Synced: 2024-08-05T11:12:37.254Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 74.4 MB
• Stars: 21
• Watchers: 7
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Chem 1000: Mathematics for Chemistry

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/ghutchis/chem1000/blob/main)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ghutchis/chem1000/main)

Materials by Prof. Geoffrey Hutchison, [Department of Chemistry, University of Pittsburgh](https://www.chem.pitt.edu/)

https://github.com/ghutchis/chem1000

This course is intended for sophomore and junior chemistry majors with a background of some calculus to prepare for physical and analytical chemistry courses. It presents mathematical topics and some minimal programming in Python / Jupyter  relevant to chemists with chemical applications.

In particular, these notebooks are intended to foster skills in computational mathematics and computational thinking.

**Overview:** 

- How does one calculate the concentration of chemical reactants and products as a function of time? 

- How does Fourier transform spectroscopy work? 

- How do molecular orbitals get their shapes?

Mathematical tools are essential across chemistry. In this class, we will survey the most important mathematical methods for chemists and illustrate applications to problems from across the chemical field.

**Learning Objectives:** After finishing the course, students should be able to apply mathematical tools to common problems in physical and analytical chemistry, including solving ordinary and partial differential equations, performing Fourier transforms, calculating differentials, solving integrals, optimizing functions, working with complex numbers, vectors, matrices, and eigenvalues. *Phew,* that’s a lot. Students should be able to solve basic chemistry-related mathematical problems using the Python programming language and use Jupyter notebooks.

The lecture materials, homework, and notebooks are intended as a supplement to *Mathematical Methods for Molecular Science* by Prof. John Straub, Boston University: http://sites.bu.edu/straub/mathematical-methods-for-molecular-science/

Note that the tone presented in the notebooks is often informal. If you use for your classes, you may wish to alter to taste.

Issues, mistakes, and bugs may occur. Please contact me or submit a GitHub issue.

This work is supported by the National Science Foundation award CHE-1800435.

**Attribution / Influencces:** Unless otherwise noted in a notebook, this work is written by me. That said, I greatly appreciate open source notebooks from a variety of people:

- [John Kitchin, Carnegie Mellon](https://github.com/jkitchin/f19-06623)

- [David Nero, Pitt](https://github.com/davidnero/phys1321-lectures)

- [Patrick Walls, UBC](https://github.com/patrickwalls/mathematical-python)

- [Charles Weiss](https://doi.org/10.1021/acs.jchemed.7b00078)

- [ESCIP Workshop](https://escip.github.io)


This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.