Thermo-Calc Educational Package
23 Mar 2021 | Tools | Contributor(s): Paul Mason, Alejandro Strachan
Module 3: Materials Descriptors for Data Science
27 Jan 2021 | Online Presentations | 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...
Module 1: Making Data Accessible, Discoverable and Useful
27 Jan 2021 | Online Presentations | 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...
Jupyter in nanoHUB: Developing and Deploying Jupyter Tools in nanoHUB
16 Dec 2020 | Online Presentations | Contributor(s): Alejandro Strachan
This presentation is available for pre-screening. The final presentation production will be forth coming.
Feature Selection for Machine Learning
15 Dec 2020 | Tools | Contributor(s): Zachary D McClure, Alejandro Strachan
Assessing feature selection for machine learning models
Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders
13 Nov 2020 | Online Presentations | 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.
Machine learning for high entropy atomic properties
26 Oct 2021 | Tools | Contributor(s): Mackinzie S Farnell, Zachary D McClure, Alejandro Strachan
Explore machine learning models used to assess the variations in local atomic properties in high entropy alloys.
PolymerXtal - Polymer Crystal Structure Generator and Analysis Software
15 Dec 2020 | Tools | Contributor(s): Tongtong Shen, Jessica Nash, Alejandro Strachan
PolymerXtal is a software designed to build and analyze molecular-level polymer crystal structures.
PNJunction Lab Exploration Tool
06 Oct 2020 | Tools | Contributor(s): Daniel Mejia, Alejandro Strachan, Saaketh Desai
DFT Results Explorer
17 Feb 2021 | Tools | Contributor(s): Saaketh Desai, Juan Carlos Verduzco Gastelum, Daniel Mejia, Alejandro Strachan
Use visualization tools to explore correlations in a DFT simulation results database
Introduction to nanohub remote
03 Oct 2020 | Tools | Contributor(s): Daniel Mejia, Alejandro Strachan, Saaketh Desai
Module 2: Querying Materials Data Repositories
30 Sep 2020 | Online Presentations | 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,...
Module 5: Neural Networks for Regression and Classification
01 Oct 2020 | Online Presentations | 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...
Module 4: Linear Regression Models
01 Oct 2020 | Online Presentations | 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...
Hands-On Data Science and Machine Learning in Undergraduate Education
07 Oct 2020 | Courses | 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.
Matlab Coding and Data Analysis in the Context of Radiation Hardening
01 Oct 2020 | Tools | Contributor(s): Amanda Johnston, Alejandro Strachan, Congying Wang, Adrian Nat Gentry, Zachary D McClure, Tamara J. Moore, Anne DeLion, Lukas Beebe Diehm, Zofia Marta Stawiarska
Learn Matlab coding within the context of Radiation Hardening problems.
Module 7: Active Learning for Design of Experiments
30 Sep 2020 | Online Presentations | 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...
nanoHUB: Online Simulation and Data
24 Sep 2020 | Presentation Materials | Contributor(s): Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory
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. ...
Linear Regression Young's modulus
24 Sep 2020 | Tools | Contributor(s): Michael N Sakano, Saaketh Desai, Alejandro Strachan
Use linear regression to extract Young's modulus and yield stress from stress-strain data
Simplified Apps
15 Sep 2020 | Labs | Contributor(s): Daniel Mejia, Gerhard Klimeck, Alejandro Strachan
Hands-on Introduction to Data & Machine Learning in Science and Engineering
This 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. We will discuss introductory activities designed to introduce undergraduate students to data science and advanced...
Matlab Data Analysis Using Jupyter Notebooks
24 Jul 2020 | Tools | Contributor(s): Jon Nykiel, Anna Leichty, Zachary D McClure, Alejandro Strachan, Aileen Ryan, Adrian Nat Gentry, Amanda Johnston, Tamara Jo Moore, Allen Garner, Peter Bermel
Use Jupyter Notebooks with a Matlab kernel running in the background for data analysis and intro to engineering homework problems
nanoHUB: Accelerating Innovation via User-Friendly, Online Apps, Tools, and Data
20 Jul 2020 | Online Presentations | Contributor(s): Alejandro Strachan, Gerhard Klimeck, Jared Gray West, Joseph M. Cychosz
Learn about the recent technological breakthroughs that are changing how simulations and the associated data are consumed and making simulation and data pervasive. From instant feedback via our simulation cache, to enabling the exploration of community generated results and machine learning...
Parsimonious neural networks
09 Jul 2020 | Tools | Contributor(s): Saaketh Desai, Alejandro Strachan
Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data