msepostdoc-list Hands-on workshops of machine learning May 12th and 19th, Register Now

[Stacey, Lisa A]

Forwarding on behalf of Ale Strachan:

Lisa Stacey
Administrative Assistant to the Department Head
School of Materials Engineering
Neil Armstrong Hall of Engineering
o: 765-494-4095   f: 765-494-1204
[3749DD84]<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.purdue.edu_-3Futm-5Fsource-3Dsignature-26utm-5Fmedium-3Demail-26utm-5Fcampaign-3Dpurdue&d=DwMFAg&c=l45AxH-kUV29SRQusp9vYR0n1GycN4_2jInuKy6zbqQ&r=cYZgnbkB39R9SYPCvL_MkXuih1H-IjjKyRBtQtQ6ib8&m=H5ec4nXIY6Mt-FgrmXhcB1ClcZZRWvtSm3BzPca31UE&s=ra1N_tE4A3L7IxFqllrao41XR8k0ynZZYRxh3KMpTwY&e=>

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Subject: Hands-on, free, workshop on machine learning May 12th and May 19th, 2021 at 1:30 PM EDT - registration open (limited seats)

Dear Colleagues,

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

Series information. Our series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities participnts
will be able to incorporate in 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.

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 (limited seats): https://nanohub.org/groups/ml/handsontraining

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, challenges that can come up with materials data, and potential solutions to these challenges. The core workflow we’ll introduce 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. If you want to preview the materials ahead of time you can find them on nanoHUB here: https://nanohub.org/tools/intromllab.

Date:  May 19th 2021,  1:30 PM - 2:30 PM EST
Title: Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Speaker: Ryan Jacobs, University of Wisconsin-Madison
Register here (limited seats): https://nanohub.org/groups/ml/handsontraining
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: https://github.com/uw-cmg/MAST-ML
Publication: https://doi.org/10.1016/j.commatsci.2020.109544
MAST-ML tutorials: https://github.com/uw-cmg/MAST-ML/tree/master/examples


Best,
--
Alejandro Strachan
Professor of Materials Engineering, Purdue University
Network for Computational Nanotechnology - nanohub.org<http://nanohub.org>
Center for Predictive Materials and Devices (c-PRIMED)

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