Good Morning, Please see announcement for upcoming hands-on workshop series on machine learning from Ale Strachan. Note: seats are limited and registration is open now, first workshop is next Wed., Jan. 13th. 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=> ________________________________ Subject: Hands-on, free, workshops on machine learning - registration open (limited seats) Dear Colleagues, nanoHUB is excited to announce the first four workshops for 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/Time: January 13, 2021 / 1 PM – 2 PM EST Title: Unsupervised clustering methods for image segmentation: application to scanning electron microscopy images of graphene By: Aagam Shah, Darren Adams, Sameh Tawfick, and Elif Ertekin Register here (limited seats): https://purdue.webex.com/purdue/onstage/g.php?MTID=e59389ef6d05f46ccf735e8d7... Abstract: In digital image processing and computer vision, image segmentation refers to the process of partitioning a digital image into multiple segments or related sets of pixels. This tutorial introduces you to U-Net, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a U-Net neural network, and apply it to segment scanning electron microscopy images of graphene on a substrate. Date/Time: January 20, 2021 / 1 PM – 2 PM EST Title: U-Net convolutional neural networks for image segmentation: application to scanning electron microscopy images of graphene By: Aagam Shah, Darren Adams, Sameh Tawfick, and Elif Ertekin Register here (limited seats): https://purdue.webex.com/purdue/onstage/g.php?MTID=e1891210e556e375681ad9cbd... Abstract: In digital image processing and computer vision, image segmentation refers to the process of partitioning a digital image into multiple segments or related sets of pixels. This tutorial introduces you to U-Net, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a U-Net neural network, and apply it to segment scanning electron microscopy images of graphene on a substrate. Date/Time: January 27, 2021 / 1 PM – 2 PM EST Title: Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks By: Yunxing Zuo, Chi Chen, and Shyue Ping Ong Register here (limited seats): https://purdue.webex.com/purdue/onstage/g.php?MTID=e43ba4d6399d343e2cde9aa0c... Abstract: This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional operations are carried out on the materials graphs. Then we will introduce the megnet package for constructing the graph network models, setting up the model for training and the model evaluation. We will show examples of graph models using computational data from the Materials Project database. Date/Time: February 3, 2021 / 1 PM – 2 PM EST Title: Convenient and efficient evelopment of Machine Learning Interatomic Potentials By: Chi Chen, Yunxing Zuo, and Shyue Ping Ong Register here (limited seats): https://purdue.webex.com/purdue/onstage/g.php?MTID=e135394b9269645c5f55a869a... Abstract: This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models. Using the prepared dataset, you will learn how to build a prototype ML-IAP and use it to predict basic material properties for a multi-component system. We hope that you can attend these workshops and walk away with enough information and practical skills to kickstart your foray into deep learning methods for your research or classroom use. Best regards, Prof. Ale Strachan, Materials Engineering, Purdue University Deputy Director, nanoHUB. http://nanohub.org/ -- 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)