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 2024 and videos of the lectures are being placed on the following YouTube playlist https://youtube.com/playlist?list=PLL0SWcFqypCl4lrzk1dMWwTUrzQZFt7y0
| month | day | Subject to cover | Assignment | Link |
|---|---|---|---|---|
| Jan | 9 | Syllabus. What is machine learning? How are materials discovered? | Install software packages together in class | |
| Jan | 11 | Machine Learning vs Materials Informatics, In class example of fitting Hall-Petch data with linear model | Read 5 High Impact Research Areas in ML for MSE (paper1), Read ISLP Chapter 3, but especially Section 3.1 | paper1, ISLP |
| Jan | 16 | Materials data repositories, get pymatgen running for everybody, examples of MP API, MDF, NOMAD, others | Create a new env and make sure you can get the notebooks in the "worked examples/MP_API_example" and "worked examples/foundry" folders running. | Materials Project API |
| Jan | 18 | Machine Learning Tasks and Types, Featurization in ML, Composition-based feature vector | Read Is domain knowledge necessary for MI (paper1). Make sure you can get the CBFV_example notebook running in the ""worked examples/CBFV_example" folder | paper1 |
| Jan | 23 | Classification and cross-validation | Read ISLP Sections 4.1-4.5 and Section 5.1. Run through classification notebook | ISLP |
| Jan | 25 | Structure-based feature vector, crystal graph networks, SMILES vs SELFIES, 2pt statistics | read selfies (paper1), two-point statistics (paper2) and intro to graph networks (blog1) | paper1, paper2, blog1 |
| Jan | 30 | Simple linear/nonlinear models. test/train/validation/metrics | Read linear vs non-linear (blog1), read best practices (paper1), benchmark dataset (paper2), and loco-cv (paper3). | blog1, paper1, paper2, paper3 |
| Feb | 1 | in-class examples of featurization | Run through 2pt statistics, GridRDF, CBFV notebooks | HW1 due! |
| Feb | 6 | ensemble models, ensemble learning | Read ensemble (blog1), and ensemble learning (paper1) | blog1, paper1 |
| Feb | 8 | Extrapolation, support vector machines, clustering | Read extrapolation to extraordinary materials (paper1), clustering (blog1) , SVMs (blog2) | paper1, blog1, blog2 |
| Feb | 13 | Artificial neural networks | Read the introduction to neural networks (blog1, blog2) | blog1, blog2 |
| Feb | 15 | Advanced deep learning (CNNs, RNNs) | HW2 due. Read… | blog1, blog2 |
| Feb | 20 | Transformers | Read the introduction to transformers (blog1, blog2) | blog1, blog2 |
| Feb | 22 | Generative ML: Generative Adversarial Networks and variational autoencoders | Read about VAEs (blog1, blog2, repo1) and GANS () | blog1, blog2, repo1 |
| Feb | 27 | Diffusion models and Image segmentation | Read U-net (paper1) and nuclear forensics (paper2) | CrysTens repo |
| Feb | 29 | Image segmentation part 2 and in-class coding examples | Download CrysTens github repo, read Segment Anything Model (paper 3) | paper1, paper2, paper3 |
| Mar | 5 | NO CLASS, spring break | ||
| Mar | 7 | No CLASS, spring break | ||
| Mar | 12 | Bayesian Inference | Read the introduction to Bayesian (blog1), go through Naive Bayes notebook | blog1 |
| Mar | 14 | Gaussian Processes | ||
| Mar | 19 | Bayesian Optimization | ||
| Mar | 21 | Self Driving labs part 1 | ||
| Mar | 26 | Self Driving labs part 2 | ||
| Mar | 28 | Large Language Models part 1 | TBD | TBD |
| Apr | 2 | NO CLASS | TBD | TBD |
| Apr | 4 | Large Language Models part 2 | TBD | TBD |
| Apr | 9 | Case study: CoCoCrab and BRDA | Read CoCoCrab (paper1) and BRDA (paper2) | paper1, paper2 |
| Apr | 11 | Case study: CrabNET vs Roost | Read CrabNet (paper1) and Roost (paper2) | paper1, paper2 |
| Apr | 16 | Case study: Superhard materials, structure prediction | Read superhard (paper1), and structure prediction papers (paper2) | paper1, paper2 |
| Apr | 18 | Case study: CGCNN vs MEGNET vs SchNET | Read CGCNN (paper1), MegNET (paper2), SchNET (paper3) | paper1, paper2, paper3 |
| Apr | 20 | Case study: ElMD & Mat2Vec | Final project due. Read Mat2Vec (paper1) and ElMD (paper2) | paper1, paper2 |
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I can recommend the book Introduction to Machine Learning found here https://www.statlearning.com/