Tags: NCN Group - Machine Learning

Description

Education group: Machine Learning.

Machine Learning.

All Categories (1-20 of 46)

  1. 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.

  2. 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.

  3. 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...

  4. Materials Graph Network

    27 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo

    Materials Graph Networks for molecule and crystal structure-property relationship modeling

  5. 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...

  6. Machine Learning Force Field for Materials

    25 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo

    Machine learning force field for materials

  7. SEM Image Segmentation Workshop

    12 Jan 2021 | | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, E. Ertekin

    This tool introduces users to machine learning used to segment microscopy images

  8. 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.

  9. 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.

  10. Machine Learning Defect Behavior in Semiconductors

    10 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi

    Develop machine learning models to predict defect formation energies in chalcogenides

  11. 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.

  12. 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. ...

  13. nanoHUB: Online Simulation and Data

    24 Sep 2020 | | Contributor(s):: Alejandro Strachan

    These slides introduce nanoHUB, an open platform for online simulations and collaboration.

  14. 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

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. Hands-on Supervised Learning: Part 1 - Linear Regression and Neural Networks

    22 Apr 2020 | | Contributor(s):: Saaketh Desai

    This tutorial introduces supervised learning via Jupyter notebooks on nanoHUB.org. You will learn how to setup a basic linear regression in a Jupyter notebook and then create and train a neural network. The tool used in this demonstration is Machine Learning for Materials Science:...