Part 1: Using Python to access and process crystal structures

** Check out my machine learning model in action here: https://sheriftawfikabbas.github.io/ **
** Check out Oganesson, the new python package that makes AI for materials research easier: https://github.com/sheriftawfikabbas/oganesson **
** Enjoy my new free ad-free board game, which is fun for the arithmetic-savvy: https://play.google.com/store/apps/details?id=adzeina.billions **

This is a series of 2 online tutorials that cover the basics of using machine learning for material science. I will walk you through a few Python scripts that will enable you to classify materials and predict their properties.

These tutorials are targeted to Masters/PhD students and scientists who have some experience with crystal structure (just the basics, like lattice constants and atomic positions). I also expect you to have basic experience with programming (in any language; Python, C, C++, C#, Java, JavaScript, etc).


Note: You don't need to install Python on your computer. We will run Python together online on Google Colab. To access Colab, all you need is a Google account.


Part 1 (the present tutorial): The basics of machine learning for material science: Using Python to access and process crystal structures

* Data/time: April 14, 2020, 5:00PM (GMT+11:00) Melbourne
* Topics: Writing Python code on Google Colab notebooks. Using Python to read crystal structure files (CIF or XYZ formats) using the PyMatGen library (which we will install on Google Colab). Querying structures in the MaterialsProject.org database.

Part 2: Doing machine learning: Predicting the band gap of materials

* Date/time: to be decided (stay tuned!)
* Topics: Writing Python code to generate descriptors from the CIF files. Classifying materials based no their structures/properties. Predicting the band gap using a range of machine learning algorithms.