KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #deepseek-harness #typescript #ai-agent #codex #developer-tools #claude-code #windows #mcp #python #desktop-app

MaterialsInformatics

★ 247 Open GitHub ↗

MSE5540/6640 Materials Informatics course at the University of Utah. Learn how data science tools are revolutionizing materials science!

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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](https://youtube.com/playlist?list=PLL0SWcFqypCl4lrzk1dMWwTUrzQZFt7y0)

![My Image](https://raw.githubusercontent.com/sp8rks/MaterialsInformatics/main/YT_playlist.jpg)

| 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](https://doi.org/10.1021/acs.chemmater.9b04078)) Read ISLP Chapter 3 Section 3.1 ([ISLP](https://www.statlearning.com/)) | | |
| Jan | 13 | Materials data repositories, pymatgen, MP API | [Materials Project API](https://next-gen.materialsproject.org/api) | MP_API_example, foundry notebooks | |
| Jan | 15 | ML Tasks and Types, Featurization, CBFV | Read domain knowledge paper ([paper](https://doi.org/10.1007/s40192-020-00179-z)) | CBFV_example notebook | |
| Jan | 20* | Best Practices and Classification | Read ISLP Sections 4.1-4.5, 5.1 ([ISLP](https://www.statlearning.com/)) Best Practices paper ([paper](https://doi.org/10.1021/acs.chemmater.0c01907)) | Classification notebooks | HW1 out |
| Jan | 22* | Structure-based feature vector, crystal graphs, SMILES/SELFIES, 2pt statistics | Selfies paper ([paper](https://doi.org/10.1088/2632-2153/aba947)) Two-point statistics paper ([paper](https://linkinghub.elsevier.com/retrieve/pii/S1359645408004886)) Intro to graph networks ([blog](https://distill.pub/2021/gnn-intro/)) | | |
| Jan | 27 | Linear/nonlinear models, test/train/validation | Linear vs non-linear ([blog](https://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/)) Benchmark dataset paper ([paper](https://doi.org/10.1038/s41524-020-00406-3)) LOCO-CV paper ([paper](https://doi.org/10.1039/C8ME00012C)) | | |
| Jan | 29 | Featurization in-class coding + case study | | 2pt statistics, RDKit notebooks | |
| Feb | 3* | Ensemble models and learning | Ensemble methods ([blog](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)) Ensemble learning paper ([paper](https://doi.org/10.1007/s40192-020-00178-0)) | | **HW1 due!** |
| Feb | 5* | Extrapolation, SVMs, clustering | Extrapolation paper ([paper](https://doi.org/10.1016/j.commatsci.2019.109498)) Clustering/UMAP explainer ([blog](https://towardsdatascience.com/how-exactly-umap-works-13e3040e1668)) SVM guide ([blog](https://towardsdatascience.com/the-complete-guide-to-support-vector-machine-svm-f1a820d8af0b)) | | HW2 out |
| Feb | 10 | Case Study TBD + **Paper Forum I** | | | |
| Feb | 12* | Artificial neural networks | Intro to neural networks ([blog](https://towardsdatascience.com/machine-learning-for-beginners-an-introduction-to-neural-networks-d49f22d238f9)) Neural networks series ([blog](https://towardsdatascience.com/a-gentle-introduction-to-neural-networks-series-part-1-2b90b87795bc)) | | |
| Feb | 17* | Advanced deep learning (CNNs, RNNs) | CNNs guide ([blog](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53)) RNNs blog (link TBD) | | |
| Feb | 19* | Transformers | What is a transformer? ([blog](https://medium.com/inside-machine-learning/what-is-a-transformer-d07dd1fbec04)) Illustrated transformers guide ([blog](https://towardsdatascience.com/illustrated-guide-to-transformers-step-by-step-explanation-f74876522bc0)) | | **HW2 due!** |
| Feb | 24* | Generative ML (GANs, VAEs) | VAE overview ([blog](https://visualstudiomagazine.com/articles/2021/05/06/variational-autoencoder.aspx?m=1)) VAE in PyTorch ([blog](https://debuggercafe.com/getting-started-with-variational-autoencoder-using-pytorch/)) PyTorch-VAE repo ([repo](https://github.com/AntixK/PyTorch-VAE)) U-net paper ([paper](https://arxiv.org/pdf/1505.04597.pdf)) Nuclear forensics paper ([paper](https://doi.org/10.1016/j.jnucmat.2019.01.042)) | | HW3 out |
| Feb | 26 | Diffusion models | Segment Anything Model ([paper](https://arxiv.org/abs/2304.02643)) | [CrysTens repo](https://github.com/michaeldalverson/CrysTens) | |
| 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](https://distill.pub/2019/visual-exploration-gaussian-processes/)) | Naive Bayes notebook | HW4 out |
| Mar | 26 | Gaussian Processes | Gaussian processes visual explainer ([blog](https://distill.pub/2019/visual-exploration-gaussian-processes/)) | | | 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/](https://www.statlearning.com/)

MaterialsInformatics

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README