andrewfalkowski / materialsinformatics

MSE5540/6640 Materials Informatics course at the University of Utah

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

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	Support vector machines, ensemble models	HW1 due. Read SVM (blog1) and ensemble (blog2)	blog1, blog2
Feb	6	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	13	NO CLASS Sparks at LLM in Chemistry workshop
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	29	Image segmentation part 1	Read U-net (paper1) and nuclear forensics (paper2)	paper1, paper2
Mar	5	NO CLASS, spring break
Mar	7	No CLASS, spring break
Mar	12	Bayesian Inference	HW3 due. Read the introduction to Bayesian (blog1)	blog1
Mar	14	Image segmentation part 2	Read Segment Anything Model (paper 1)	paper1
Mar	19	Case study: Superhard materials, structure prediction	Read superhard (paper1), and structure prediction papers (paper2)	paper1, paper2
Mar	21	Case study: CGCNN vs MEGNET vs SchNET	Read CGCNN (paper1), MegNET (paper2), SchNET (paper3)	paper1, paper2, paper3
Mar	26	Case study: CrabNET vs Roost	Read CrabNet (paper1) and Roost (paper2)	paper1, paper2
Mar	28	Case study: Cococrab, BRDA	HW4 due. Read Cococrab (paper1) and BRDA (paper2)	paper1, paper2
Apr	2	Large Language Models part 1	TBD	TBD
Apr	4	Large Language Models part 2	TBD	TBD
Apr	9	Case study: Element Mover’s Distance, Mat2Vec	Read Element mover’s distance (paper1) and Mat2Vec (paper2)	paper1, paper2
Apr	11	Case study: Discover algorithm, Robocrystallographer	TBD	TBD
Apr	16	Final project presentation day 1	Final Project due
Apr	18	Final project presentation day 2	Final Project due

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