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 5050/7050 "Materials Informatics" taught at the University of Utah in the Materials Science & Engineering department.
Below you'll find the approximate calendar for Spring 2022 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 | 11 | Syllabus. What is machine learning? How are materials discovered? | ||
| Jan | 13 | Machine Learning vs Materials Informatics, Materials Data repositories | Read 5 High Impact Research Areas in ML for MSE (paper1) and materials databases (paper2). | paper1, paper2 |
| Jan | 18 | Get pymatgen running for everybody, MP API example, Citrination example, others | Download pymatgen ahead of class if possible | pymatgen |
| Jan | 20 | Machine Learning Tasks and Types, Featurization in ML, Composition-based feature vector | Read Is domain knowledge necessary for MI (paper1) | paper1 |
| 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 | 27 | Simple linear/nonlinear models. test/train/validation/metrics | HW1 due. Read linear vs non-linear (blog1), read best practices (paper1), benchmark dataset (paper2), and loco-cv (paper3). | blog1, paper1, paper2, paper3 |
| Jan | 1 | Support vector machines, ensemble models | Read SVM (blog1) and ensemble (blog2) | blog1, blog2 |
| Feb | 3 | Extrapolation, ensemble learning, clustering | Read extrapolation to extraordinary materials (paper1), ensemble learning (paper2), clustering (blog1) | paper1, paper2, blog1 |
| Feb | 8 | Artificial neural networks | Read the introduction to neural networks (blog1, blog2) | blog1, blog2 |
| Feb | 10 | Advanced deep learning (CNNs, RNNs) | HW2 due. Read… | blog1, blog2 |
| Feb | 15 | Transformers | Read the introduction to transformers (blog1, blog2) | blog1, blog2 |
| Feb | 17 | Generative ML: Generative Adversarial Networks and variational autoencoders | Read about VAEs (blog1, blog2, repo1) and GANS () | blog1, blog2, repo1 |
| Feb | 22 | Image segmentation | TBD | TBD |
| Feb | 24 | Bayesian Inference | HW3 due. Read the introduction to Bayesian (blog1) | blog1 |
| Feb | 29 | TMS meeting, NO CLASS | ||
| Mar | 3 | Dr. Sparks at TMS meeting, Dr. Luther McDonald will provide guest lecture | TBD | TBD |
| Mar | 8 | Spring Break, NO CLASS | ||
| Mar | 10 | Spring Break, NO CLASS | ||
| Mar | 15 | Dr. Sparks at APS Meeting, Dr. Tolga Tasdizen will provide guest lecture | Read U-net (paper1) and nuclear forensics (paper2) | paper1, paper2 |
| Mar | 17 | APS meeting, NO CLASS | ||
| Mar | 22 | Case study: Superhard materials, structure prediction | Read superhard (paper1), and structure prediction papers (paper2) | paper1, paper2 |
| Mar | 24 | Case study: CGCNN vs MEGNET vs SchNET | Read CGCNN (paper1), MegNET (paper2), SchNET (paper3) | paper1, paper2, paper3 |
| Mar | 29 | Case study: CrabNET vs Roost | Read CrabNet (paper1) and Roost (paper2) | paper1, paper2 |
| Mar | 31 | Case study: Cococrab, BRDA | HW4 due. Read Cococrab (paper1) and BRDA (paper2) | paper1, paper2 |
| Apr | 5 | Dr. Sparks at AIM 2022 meeting, Dr. Jake Hochalter will provide guest lecture. | Explainable/interpretable ML, physics-informed modeling | TBD |
| Apr | 7 | Dr. Sparks at AIM 2022 meeting, Dr. Ben Blaiszik of MDF will provide guest lecture. | MDF | TBD |
| Apr | 12 | Case study: Element Mover’s Distance, Mat2Vec | Read Element mover’s distance (paper1) and Mat2Vec (paper2) | paper1, paper2 |
| Apr | 14 | Case study: Discover algorithm, Robocrystallographer | TBD | TBD |
| Apr | 19 | Final project presentation day 1 | Final Project due | |
| Apr | 21 | Dr. Sparks at AMRAD meeting, Dr. Ashley Spear will provide guest lecture | TBD | TBD |
| Apr | 26 | Final project presentation day 2 | Final Project due | |
I can recommend the book Introduction to Machine Learning found here https://www.statlearning.com/