Machine Learning Workshops - limited seating

 

NanoHUB is excited to announce the nest two workshops in the Spring 2021 session of the Hands-on Data Science and Machine Learning Training Series. 

 

Series information. The series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities that participants

will be able to incorporate into their work. Hands-on activities will use nanoHUB cloud computing resources; no need to download or install any software. All you 

need is an internet connection and a browser. After the training sessions, you will be able to continue using nanoHUB for research or education.

 

Registration links and material for prior workshops can be found at the workshop webpage

https://nanohub.org/groups/ml/handsontraining

 

Register soon as seats are limited. 

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Date: May 12th 2021,  1:30 PM - 2:30 PM EST

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

Speaker: Benjamin Afflerbach, University of Wisconsin-Madison

 

Register here

 

Abstract. This workshop will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions. As we progress through this workflow, we’ll highlight key steps and challenges that can come up with materials data, and potential solutions to these challenges. The core workflow we’ll introduce includes:

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. 

 

If you want to preview the materials ahead of time you can find them on nanoHUB here.

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Register here (limited seats):  

 

Abstract. This tutorial contains an introduction to the use of 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).

 

MAST-ML code

Publication

MAST-ML tutorials