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Characterizing Electrolytic Materials
Teaching Materials | 31 Mar 2021 | Contributor(s):: Steven Kandel, NNCI Nano
The lab is designed to help students understand that the resistance of an object depends on length, cross-sectional area, and the type of material. Students measure the current through objects to see that different materials resist current in different amounts. Students will find that,...
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Thermo-Calc Educational Package
Tools | 23 Mar 2021 | Contributor(s):: Paul Mason, Alejandro Strachan
Thermo-Calc Educational Package
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Convenient and efficient development of Machine Learning Interatomic Potentials
Online Presentations | 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.
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
Online Presentations | 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.
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MIT Atomic-Scale Modeling Toolkit
Tools | 15 Jan 2008 | Contributor(s):: David A Strubbe, Enrique Guerrero, daniel richards, Elif Ertekin, Jeffrey C Grossman, Justin Riley
Tools for Atomic-Scale Modeling
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Symposium on Nanomaterials for Energy: Atomic Force Microscopy for Energy Applications - A Review
Online Presentations | 17 May 2012 | Contributor(s):: Arvind Raman
Atomic Force Microscopy is unique in its ability to measure sub -nanonewton forces arising from a variety physical phenomena between a sharp tip and a sample. In this talk we review the most recent applications of atomic force microscopy to explore and characterize quantitatively the properties...
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Shape-changing Nanoparticles for Nanomedicine Applications
Online Presentations | 04 Feb 2021 | Contributor(s):: Vikram Jadhao
I will describe the nanoHUB tool “Nanoparticle Shape Lab” that enables simulations of the shape deformation of charged nanoparticles for a broad variety of nanoparticle material properties including nanoparticle surface charge, pattern, elasticity and solution conditions such...
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Feb 03 2021
Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
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...
https://nanohub.org/events/details/1953
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U-Net Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
Online Presentations | 26 Jan 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...
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Materials Graph Network
Tools | 21 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo
Materials Graph Networks for molecule and crystal structure-property relationship modeling
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Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
Online Presentations | 19 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...
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Module 1: Making Data Accessible, Discoverable and Useful
Online Presentations | 26 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 | 26 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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Machine Learning Force Field for Materials
Tools | 27 Oct 2020 | Contributor(s):: Chi Chen, Yunxing Zuo
Machine learning force field for materials
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SEM Image Segmentation Workshop
Tools | 10 Dec 2020 | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin
This tool introduces users to machine learning used to segment microscopy images
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Teaching Engineering using Jupyter Notebooks
Online Presentations | 13 Aug 2020 | Contributor(s):: Susan P Gentry, Rei Sanchez-Arias, David R. Ely, Jon Nykiel, Cindy Nguyen
This talk discusses the use of Jupyter Notebooks on nanoHUB for teaching materials engineering.
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Machine Learning Framework for Impurity Level Prediction in Semiconductors
Online Presentations | 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.
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PolymerXtal - Polymer Crystal Structure Generator and Analysis Software
Tools | 13 Oct 2020 | Contributor(s):: Tongtong Shen, Jessica Nash, Alejandro Strachan
PolymerXtal is a software designed to build and analyze molecular-level polymer crystal structures.
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"Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
Series | 15 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry
Expanding on the pre-existing resource on nanoHUB: “Turning Fruit Juice into Graphene Quantum Dots” this resource expands on the concepts in the experimental guide to give a comprehensive overview of materials pertaining to concepts and ideas within the...
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MODULE 3 - Structures: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
Teaching Materials | 15 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry
In MODULE 3- Structures in the "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, crystal structures and systems are investigated. This module relates back to graphene and how its structure relates back to its unique properties in comparison to other forms of...