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Hands-On Data Science and Machine Learning in Undergraduate Education
Courses | 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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Module 5: Neural Networks for Regression and Classification
Online Presentations | 01 Oct 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown...
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Module 2: Querying Materials Data Repositories
Online Presentations | 30 Sep 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan
This module introduces modern tools for data acquisition, including performing large queries using application programming interfaces (APIs), with hands-on online workflows. Cyber-infrastructure platforms for data offer unparalleled access to data, this module will introduce tools to manage,...
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Module 7: Active Learning for Design of Experiments
Online Presentations | 30 Sep 2020 | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module introduces active learning in the context of materials discovery with hands-on online simulations. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore...
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Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory
Presentation Materials | 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
Presentation Materials | 24 Sep 2020 | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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Probabilistic Computing: From Materials and Devices to Circuits and Systems
Online Presentations | 07 Sep 2020 | Contributor(s):: Kerem Yunus Camsari
In this talk, I will describe one such path based on the concept of probabilistic or p-bits that can be scalably built with present-day technology used in magnetic memory devices.
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Uncertainty Quantification and Scientific Machine Learning for Complex Engineering Systems
Online Presentations | 17 Aug 2020 | Contributor(s):: Guang Lin
In this talk, I will first present a review of the novel UQ techniques I developed to conduct stochastic simulations for very large-scale complex systems.
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ECG Data Analysis Using Machine Learning
Tools | 03 Aug 2020 | Contributor(s):: Rebecca Mosier, Guang Lin
Perform data analysis on ECG data using machine learning methods.
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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
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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
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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
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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:...
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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
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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
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ECE 595ML: Course Overview
Online Presentations | 28 May 2020 | Contributor(s):: Stanley H. Chan
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ECE 595ML Lecture 1.2: Linear Regression - Geometry
Online Presentations | 28 May 2020 | Contributor(s):: Stanley H. Chan
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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...
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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...
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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...