edwardgadasu/materialsinformatics

MSE5050/7050 Materials Informatics course at the University of Utah

MaterialsInformatics

MSE5050/7050 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