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) Read ISLP Chapter 3 Section 3.1 (ISLP) |
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| 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) Best Practices paper (paper) |
Classification notebooks | HW1 out |
| Jan | 22* | Structure-based feature vector, crystal graphs, SMILES/SELFIES, 2pt statistics | Selfies paper (paper) Two-point statistics paper (paper) Intro to graph networks (blog) |
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| Jan | 27 | Linear/nonlinear models, test/train/validation | Linear vs non-linear (blog) Benchmark dataset paper (paper) LOCO-CV paper (paper) |
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| Jan | 29 | Featurization in-class coding + case study | 2pt statistics, RDKit notebooks | HW1 due! | |
| Feb | 3* | Ensemble models and learning | Ensemble methods (blog) Ensemble learning paper (paper) |
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| Feb | 5* | Extrapolation, SVMs, clustering | Extrapolation paper (paper) Clustering/UMAP explainer (blog) SVM guide (blog) |
HW2 out | |
| Feb | 10 | Case Study TBD + Paper Forum I | |||
| Feb | 12* | Artificial neural networks | Intro to neural networks (blog) Neural networks series (blog) |
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| Feb | 17* | Advanced deep learning (CNNs, RNNs) | CNNs guide (blog) RNNs blog (link TBD) |
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| Feb | 19* | Transformers | What is a transformer? (blog) Illustrated transformers guide (blog) |
HW2 due! | |
| Feb | 24* | Generative ML (GANs, VAEs) | VAE overview (blog) VAE in PyTorch (blog) PyTorch-VAE repo (repo) U-net paper (paper) Nuclear forensics paper (paper) |
HW3 out | |
| Feb | 26 | Diffusion models part 1 | Segment Anything Model (paper) | CrysTens repo | |
| Mar | 3 | Diffusion models part 2 + Image segmentation part 1 | coding examples | ||
| Mar | 5 | Image segmentation part 2 | HW 3 due! | ||
| Mar | 10 | No CLASS, spring break | |||
| Mar | 12 | No CLASS, spring break | |||
| Mar | 17* | Bayesian Inference | Intro to Bayesian / Gaussian processes visual explainer (blog) | Naive Bayes notebook | |
| Mar | 19* | Gaussian Processes | Gaussian processes visual explainer (blog) | Final Project Briefing | |
| Mar | 24 | Bayesian Optimization in-class coding + case study | |||
| Mar | 26 | No CLASS, TMS Meeting | |||
| Mar | 31 | No CLASS, TMS Meeting | |||
| Apr | 2 | Large Language Models part 1 | |||
| Apr | 7 | Large Language Models part 2 + Intro to Agentic AI part 1 | |||
| Apr | 9 | Intro to Agentic AI part 2 | |||
| Apr | 14 | Crash Course: Autonomous Materials Science w/ Self-Driving Labs | |||
| 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/