The current README.md serves as a tutorial and documentation - last update January 20, 2025

Welcome to my self-study course on computational chemistry and data analysis! This repository is designed as an accessible starting point for undergraduate students or early learners interested in applying computational methods to chemical problems. The course emphasizes practical, hands-on learning and is particularly valuable for those who lack prior research experience. It aims to demystify computational tools like Pymatgen and make them approachable for students outside of computer science disciplines.

This is a work-in-progress, therefore, expect modules to change either by order or contents.

• Undergraduate students in chemistry or related fields.
• Enthusiasts eager to explore computational tools without extensive prior knowledge of coding or computer science.
• Learners interested in improving their ability to work with and analyze data in a chemistry context.

• Introduce computational chemistry concepts and tools, focusing on practicality and ease of use.
• Provide a scaffolded learning experience with clear progression from basics to more advanced applications.
• Equip students with skills to manage, analyze, and visualize data effectively in Python.

This course is structured into modules, each containing the following components:

1. Learning Material:
   Concise explanations of concepts, tools, and techniques.

2. Examples & Demos:
   Code snippets and walkthroughs showcasing applications in computational chemistry.

3. Practice Questions:
   Targeted questions to test your understanding of the material.

4. Assignments:
   Mini-projects or challenges designed to deepen your learning through application.

5. Solutions & Discussion:
   Suggested answers and explanations to help you reflect on your work.

• Installing Python and Anaconda.
• Setting up your Python environment.

• Overview of computational chemistry and its applications.
• Introduction to key libraries: Pymatgen, Numpy, Pandas, and Matplotlib.
• Best practices

• Representing crystal structures using Pymatgen.
• Parsing and visualizing CIF files.
• Different softwares.
• Basic symmetry operations, lattice parameters and jargon.

• Extracting material properties.
• Data cleaning and manipulation with Pandas.
• Creating meaningful visualizations.

• Setting up batch operations for structure generation.
• Automating data extraction and analysis workflows.

• Integrate everything you’ve learned to solve a real-world computational chemistry problem.

Many students find traditional computational chemistry resources intimidating, particularly those with heavy programming prerequisites. This course offers:

• A beginner-friendly approach with no prior experience required.
• Hands-on practice to build confidence and competence.
• A modular design so you can learn at your own pace.

1. Clone the Repository:

   $ git clone github.com/OliynykLab/Materials-Informatics-Courses.git
   $ pip install -r requirements.txt
   $ cd Materials-Informatics-Course

2. Follow the course modules in order or jump to topics of interest.
3. Use the provided examples and demos to experiment and learn interactively.
4. Complete assignments and explore optional challenges for deeper engagement.

This course was visualized by Dr. Oliynyk to expand computational techniques with an absolute-zero background. The shaping of this course was inspired by my experiences as an undergraduate student navigating computational research without prior exposure. I hope it serves as a valuable resource for anyone eager to dive into this field.

• Anton Oliynyk
• Balaranjan Selvaratnam
• Danila Shiryaev
• Emil Jaffal

• If you have any issues or questions, please feel free to reach out or leave an issue.