Join nanoHUB for a two-part set of  back-to-back ML workshops. Part I is an introduction to Machine Learning for Materials Science that is appropriate for people with no prior experience who would like to start teaching machine learning concepts.  Part II is a more advanced, complementary training with the Materials Simulation Toolkit for Machine Learning (MAST-ML). The workshops form a series, but you are welcome to join just one if you like.

Click here to register

Date and Time

Tuesday, September 13, 2022 / 1:00 PM – 3:00 PM EDT

Part I

An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties

Dr. Benjamin Afflerbach, University of Wisconsin- Madison

Part one of the workshop will introduce core concepts of machine learning through the lens of a basic workflow to predict material band gaps from their compositions. As we progress through this workflow we will highlight key steps, challenges that can come up with materials data, and potential solutions to these challenges. The core workflow includes: Data Cleaning, Feature Generation, Feature Engineering, Establishing Model Assessment, Training a Default Model, Hyperparameter Optimization, and Making Predictions. By the end of the workshop I hope that you’ll have a better understanding of these core concepts, and how they can all fit together. 

Part II

The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction 

Dr. Ryan Jacobs, University of Wisconsin- Madison

Part two of the workshop is a tutorial that introduces the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction. Through hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of different model types and data splitting techniques, and (4) conduct a preliminary assessment of model error analysis and uncertainty quantification (UQ).

Bios

Benjamin Afflerbach, Ph.D. is a Research Associate in the Materials Science and Engineering department at the University of Wisconsin – Madison. His research uses materials informatics to supplement traditional materials science research, with a focus on metallic glasses. He is also  heavily involved in management of the Informatics Skunkworks undergraduate research program, where he has mentored multiple undergraduate research teams, developed educational materials for onboarding undergraduate researchers, and built community infrastructure to grow the group.

Ryan Jacobs, Ph.D.  is a Research Scientist in the Department of Materials Science and Engineering at the University of Wisconsin- Madison. He uses atomistic modeling, data science and machine learning (materials informatics) methods to understand the structure and properties of materials at the atomic scale in order to discover and design novel material compounds for specific technological applications. His areas of interest are materials for energy technology (solid oxide and protonic fuel cells, batteries, and solar photovoltaics) and the surface electronic and thermodynamic properties of metals and oxides used as electron emission cathodes.

1. machine learing
2. MAST-ML
3. materials science
4. tool:intromllab
5. tool:mastmltutorials