MaterialsInformatics

MSE5540/6640 Materials Informatics course at the University of Utah

This github repo contains coursework content such as class slides, code notebooks, homework assignments, literature, and more for MSE 5540/6640 "Materials Informatics" taught at the University of Utah in the Materials Science & Engineering department.

Below you'll find the approximate calendar for Spring 2026 and videos of the lectures are being placed on the following YouTube playlist:
YouTube playlist

| month | day | Subject to cover | Readings | Code/Notebooks | Assignment | |------:|:----:|------------------|----------|----------------|------------| | Jan | 6 | Syllabus, What is ML, Materials discovery | | Install software packages | | | Jan | 8 | Using Github, Hall-Petch fitting | Read 5 High Impact Research Areas in ML for MSE (paper)<br>Read ISLP Chapter 3 Section 3.1 (ISLP) | | | | Jan | 13 | Materials data repositories, pymatgen, MP API | Materials Project API | MP_API_example, foundry notebooks | | | Jan | 15 | ML Tasks and Types, Featurization, CBFV | Read domain knowledge paper (paper) | CBFV_example notebook | | | Jan | 20* | Best Practices and Classification | Read ISLP Sections 4.1-4.5, 5.1 (ISLP)<br>Best Practices paper (paper) | Classification notebooks | HW1 out | | Jan | 22* | Structure-based feature vector, crystal graphs, SMILES/SELFIES, 2pt statistics | Selfies paper (paper)<br>Two-point statistics paper (paper)<br>Intro to graph networks (blog) | | | | Jan | 27 | Linear/nonlinear models, test/train/validation | Linear vs non-linear (blog)<br>Benchmark dataset paper (paper)<br>LOCO-CV paper (paper) | | | | Jan | 29 | Featurization in-class coding + case study | | 2pt statistics, RDKit notebooks | | | Feb | 3* | Ensemble models and learning | Ensemble methods (blog)<br>Ensemble learning paper (paper) | | HW1 due! | | Feb | 5* | Extrapolation, SVMs, clustering | Extrapolation paper (paper)<br>Clustering/UMAP explainer (blog)<br>SVM guide (blog) | | HW2 out | | Feb | 10 | Case Study TBD + Paper Forum I | | | | | Feb | 12* | Artificial neural networks | Intro to neural networks (blog)<br>Neural networks series (blog) | | | | Feb | 17* | Advanced deep learning (CNNs, RNNs) | CNNs guide (blog)<br>RNNs blog (link TBD) | | | | Feb | 19* | Transformers | What is a transformer? (blog)<br>Illustrated transformers guide (blog) | | HW2 due! | | Feb | 24* | Generative ML (GANs, VAEs) | VAE overview (blog)<br>VAE in PyTorch (blog)<br>PyTorch-VAE repo (repo)<br>U-net paper (paper)<br>Nuclear forensics paper (paper) | | HW3 out | | Feb | 26 | Diffusion models | Segment Anything Model (paper) | CrysTens repo | | | Mar | 3 | Image segmentation | | coding examples | | | Mar | 5 | Crash Course: Autonomous Materials Science w/ Self-Driving Labs + Guest Lecture w/ Joseph F. Krause @Radical AI| | | Final Project Briefing | | Mar | 10 | No CLASS, spring break | | | | | Mar | 12 | No CLASS, spring break | | | | | Mar | 17 | No CLASS, TMS Meeting | | | | | Mar | 19 | No CLASS, TMS Meeting | | | HW 3 due! | | Mar | 24 | Bayesian Inference | Intro to Bayesian / Gaussian processes visual explainer (blog) | Naive Bayes notebook | HW4 out | | Mar | 26 | Gaussian Processes | Gaussian processes visual explainer (blog) | | | Final Project Group Sign Up due | | Mar | 31 | Bayesian Optimization in-class coding + case study | | | | Apr | 2 | Large Language Models part 1 | | | | | Apr | 7 | Large Language Models part 2 | | |Paper Forum II Papers Assignment | | Apr | 9 | Intro to Agentic AI part 1 | | | HW4 due! | | Apr | 14 | Intro to Agentic AI part 2 | | | | | Apr | 16 | Case Study TBD + Paper Forum II | | | | | Apr | 21 | Final project presentation | | | |

I can recommend the book Introduction to Statistical Learning found here: https://www.statlearning.com/