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The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Online Presentations | 25 Jun 2021 | Contributor(s):: Ryan Jacobs
This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction.
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tidyverse Data Science Tools for STEM Applications and Datasets
Tools | 25 Jun 2021 | Contributor(s):: Rei Sanchez-Arias
An introduction to dplyr, ggplot2, and other tidyverse data science tools in STEM applications
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Victor Wealth Adankai
https://nanohub.org/members/330788
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Sheimy Paz Serpa
https://nanohub.org/members/330719
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A Hands-on Introduction to Physics-Informed Neural Networks
Online Presentations | 16 Jun 2021 | Contributor(s):: Ilias Bilionis, Atharva Hans
Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural...
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A Hands-on Introduction to Physics-Informed Neural Networks
Tools | 21 May 2021 | Contributor(s):: Atharva Hans, Ilias Bilionis
A Hands-on Introduction to Physics-Informed Neural Networks
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Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial
Tools | 07 May 2021 | Contributor(s):: Ryan Jacobs, BENJAMIN AFFLERBACH
Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.
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DFT Results Explorer
Tools | 17 Feb 2021 | Contributor(s):: Saaketh Desai, Juan Carlos Verduzco Gastelum, Daniel Mejia, Alejandro Strachan
Use visualization tools to explore correlations in a DFT simulation results database
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Module 1: Making Data Accessible, Discoverable and Useful
Online Presentations | 27 Jan 2021 | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is...
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Module 3: Materials Descriptors for Data Science
Online Presentations | 27 Jan 2021 | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum, Zachary D McClure
This module focuses on the use of descriptors to improve the description of materials in machine learning. Augmenting input parameters with appropriate descriptors (a process sometimes called featurization) can often significantly improve the accuracy of predictive models. Ideal descriptors are...
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Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders
Online Presentations | 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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Oct 21 2020
Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders workshop
Registration for this event is now closed. Thanks for your interest!This series of workshops introduces participants to important concepts and techniques in data science and machine learning in the...
https://nanohub.org/events/details/1869
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Only Physics can save Machine Learning!
Online Presentations | 13 Oct 2020 | Contributor(s):: Muhammad A. Alam
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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 4: Linear Regression Models
Online Presentations | 01 Oct 2020 | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine...
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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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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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Portrait of a Black Hole & Beyond
Online Presentations | 26 Aug 2020 | Contributor(s):: Katie L. Bouman
Dr. Bouman, who was part of the Event Horizon Telescope team that captured the first photograph of a black hole, will talk about the challenges of the project.