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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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Bayesian optimization tutorial using Jupyter notebook
Tools | 11 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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SimTools: Delivering Simulations in the Era of Abundant Data
Online Presentations | 04 Jun 2021 | Contributor(s):: Alejandro Strachan
This presentation introduces SimTool, a library that allows developers to create, publish, and share reproducible workflows with well-defined and verified inputs and outputs.
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Model Rockets and Composite Materials: Design, Build, Launch
Teaching Materials | 27 May 2021 | Contributor(s):: Amber Genau
Students will gain experience with polymer matrix fiber composites, composite production, and the tradeoffs inherent in the engineering design process by designing, building and launching their own model rocket. Composite materials are created via hand layup and vacuum assisted resin...
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Materials for Hydrogen-Based Energy Conversion
Tools | 25 May 2021 | Contributor(s):: Nicole Shuman, Susan P Gentry
Simulate the effects different materials have on hydrogen-based energy conversion.
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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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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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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