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  1. Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride

    Tools | 03 Aug 2020 | Contributor(s):: Saswat Mishra, Karthik Guda Vishnu, Alejandro Strachan

    Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride as a function of Strain and Aluminum doping

  2. Parsimonious neural networks

    Tools | 09 Jul 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan

    Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data

  3. High Temperature Oxide Property Explorer

    Tools | 29 Jun 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan

    Explore material properties of common and niche oxide materials for high-temperature applications

  4. SEM Image Segmentation Tutorial using SEM Image Processing Tool

    Teaching Materials | 16 Jun 2020 | Contributor(s):: Joshua A Schiller

    In this activity, students will learn about the use of image processing methods to analyze Scanning Electron Microscopy images using a technique known as Image Segmentation and the SEM Image Processing Tool. The purpose of this tutorial is demonstrate several methods for image masking:...

  5. Machine Learning Lab Module

    Tools | 12 Jun 2020 | Contributor(s):: BENJAMIN AFFLERBACH, Rundong Jiang, Josh Tappan, DANE MORGAN

    A lab activity for introduction to machine learning in materials science

  6. Hands-on Deep Learning for Materials

    Tools | 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

  7. ECE 595ML: Course Overview

    Online Presentations | 28 May 2020 | Contributor(s):: Stanley H. Chan

  8. ECE 595ML Lecture 1.2: Linear Regression - Geometry

    Online Presentations | 28 May 2020 | Contributor(s):: Stanley H. Chan

  9. Kat Nykiel

    Kat Nykiel is a PhD candidate at Purdue University, currently working under the guidance of Dr. Alejandro Strachan. Her research is primarily focused on high-throughput density functional theory,...

    https://nanohub.org/members/288810

  10. Rebecca Mosier

    Rebecca Mosier is a second-year undergraduate student at Johns Hopkins University. Her majors are Biomedical Engineering and Applied Mathematics & Statistics. She is working on the Data-Driven...

    https://nanohub.org/members/288446

  11. Parsimonious Neural Networks Learn Classical Mechanics and Can Teach It

    Papers | 15 May 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan

    We combine neural networks with genetic algorithms to find parsimonious models that describe the time evolution of a point particle subjected to an external potential. The genetic algorithm is designed to find the simplest, most interpretable network compatible with the training data. The...

  12. Gaurav Arora

    https://nanohub.org/members/287812

  13. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)

    Online Presentations | 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...

  14. Hands-on Supervised Learning: Part 2 - Classification and Random Forests (2nd offering)

    Online Presentations | 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...

  15. PennyLane - Automatic Differentiation and Machine Learning of Quantum Computations

    Online Presentations | 29 Apr 2020 | Contributor(s):: Nathan Killoran

    PennyLane is a Python-based software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.

  16. Nathan Killoran

    Nathan holds a MSc in Mathematics from the University of Toronto and a PhD in Physics from the University of Waterloo. He specializes in quantum computing, deep learning, and quantum optics.

    https://nanohub.org/members/286348

  17. Machine Learning for Chemical Sensing

    Online Presentations | 29 Apr 2020 | Contributor(s):: Bruno Ribeiro

  18. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)

    Online Presentations | 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...

  19. Hands-on Sequential Learning and Design of Experiments

    Online Presentations | 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...

  20. Image Segmentation for Graphene Images

    Online Presentations | 29 Apr 2020 | Contributor(s):: Joshua A Schiller

    This lecture outlines the need for a fast, automated means for identifying regions of images corresponding to graphene. Simple methods, like color masking and template matching, are discussed initially. Unsupervised clustering methods are then introduced as potential improvements...