Skip to content

MSE5540/6640 Materials Informatics course at the University of Utah

License

Notifications You must be signed in to change notification settings

sp8rks/MaterialsInformatics

Repository files navigation

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 2024 and videos of the lectures are being placed on the following YouTube playlist https://youtube.com/playlist?list=PLL0SWcFqypCl4lrzk1dMWwTUrzQZFt7y0

My Image

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

| | | | | |

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

About

MSE5540/6640 Materials Informatics course at the University of Utah

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •  

Languages