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Convenient and efficient development of Machine Learning Interatomic Potentials
09 Mar 2021 | | Contributor(s):: Yunxing Zuo
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.
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
09 Mar 2021 | | Contributor(s):: Chi Chen
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.
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U-Net Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
01 Feb 2021 | | Contributor(s):: Aagam Rajeev Shah
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...
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Materials Graph Network
27 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo
Materials Graph Networks for molecule and crystal structure-property relationship modeling
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Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
27 Jan 2021 | | Contributor(s):: Aagam Rajeev Shah
This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a...
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Machine Learning Force Field for Materials
25 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo
Machine learning force field for materials
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SEM Image Segmentation Workshop
12 Jan 2021 | | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin
This tool introduces users to machine learning used to segment microscopy images
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Machine Learning Framework for Impurity Level Prediction in Semiconductors
15 Dec 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi
In this work, we perform screening of functional atomic impurities in Cd-chalcogenide semiconductors using high-throughput computations and machine learning.
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Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders
13 Nov 2020 | | Contributor(s):: Vinay Hegde, Alejandro Strachan
This tutorial introduces deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint.
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Machine Learning Defect Behavior in Semiconductors
10 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)
Develop machine learning models to predict defect formation energies in chalcogenides
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Hands-On Data Science and Machine Learning in Undergraduate Education
07 Oct 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai, Juan Carlos Verduzco Gastelum, Michael N Sakano, Zachary D McClure, Joseph M. Cychosz, Jared Gray West
This series of modules introduce key concepts in data science in the context of application in materials science and engineering.
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Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...
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nanoHUB: Online Simulation and Data
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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Neural Network Visualization Tool
21 Sep 2020 | | Contributor(s):: David Enrique Farache, Alejandro Strachan, Juan Carlos Verduzco Gastelum, Saaketh Desai
Develops a graph of the neural network that can be manipulated to isolate data
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Hands-on Deep Learning for Materials
10 Jun 2020 | | Contributor(s):: Saaketh Desai, Edward Kim, Vinay Hegde
This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)
30 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (2nd offering)
30 Apr 2020 | | Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)
29 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
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Hands-on Sequential Learning and Design of Experiments
29 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (1st offering)
24 Apr 2020 | | Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...