MSE5050/7050 Materials Informatics course at the University of Utah

Overview

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
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