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IWCN 2021: Recursive Open Boundary and Interfaces Method for Material Property Predictions
14 Jul 2021 | | Contributor(s):: James Charles, Sabre Kais, Tillmann Christoph Kubis
In this presentation, we show that assuming periodicity elevates a small perturbation of a periodic cell into a strong impact on the material property prediction. Periodic boundary conditions can be applied on truly periodic systems only. More general systems should apply an open boundary...
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An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
25 Jun 2021 | | Contributor(s):: Benjamin Afflerbach
This tutorial will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions.
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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
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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Parsimonious Neural Networks Learn Interpretable Physical Laws
21 Jun 2021 | | Contributor(s):: Saaketh Desai
Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary...
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Carbon Nanotube Fracture
27 May 2021 | | Contributor(s):: Christine M Aikens, George C. Schatz, Marcelo Carignano
Due to their mechanical properties, carbon nanotubes (CNTs) hold promise as nanoreinforcements in a variety of composites. As a result, numerous theoretical and experimental studies have been performed in order to understand the behavior of CNTs under axial tension. Whereas quantum mechanical...
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Convenient and efficient development of Machine Learning Interatomic Potentials
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
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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Symposium on Nanomaterials for Energy: Atomic Force Microscopy for Energy Applications - A Review
05 Feb 2021 | | 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
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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Materials Graph Network
27 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo
Materials Graph Networks for molecule and crystal structure-property relationship modeling
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Machine Learning Force Field for Materials
25 Jan 2021 | | Contributor(s):: Chi Chen, Yunxing Zuo
Machine learning force field for materials
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Machine Learning Framework for Impurity Level Prediction in Semiconductors
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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Machine Learning Defect Behavior in Semiconductors
10 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)
Develop machine learning models to predict defect formation energies in chalcogenides
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Linear Regression Young's modulus
24 Sep 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
Use linear regression to extract Young's modulus and yield stress from stress-strain data
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The Eötvös Paradox: The Enduring Significance of Eötvös' Most Famous Experiment
23 Aug 2020 | | Contributor(s):: Ephraim Fischbach
Here we summarize the key elements of this "Eötvös paradox," and suggest some possible paths to a resolution. Along the way we also discuss the close relationship between Eötvös and Einstein, and consider how their respective contributions may have been influenced...
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Aerogel (Aerogel)
02 Jul 2020 | | Contributor(s):: Nano-Link Center for Nanotechnology Education, Carol E. Bouvier, James J Marti, Rodfal Alberto Rodriguez (editor), María Teresa Rivera (editor)
Los aerogeles son las sustancias semisólidas conocidas más livianas que existen. Los aerogeles de sílice o dióxido de silicio (SiO2) tienen la misma estructura química que la arena de sílice o el vidrio de las ventanas, pero tienen diferencias...
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Mark Knackstedt
Mark Knackstedtis Professor at the Department of Applied Mathematics at the Australian National University (ANU). He received his BSc degree at Columbia University and PhD in Chemical Engineering...
https://nanohub.org/members/287595
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Mystery Molecules: Identifying Materials with Nanoscale Characterization Tools
18 Mar 2020 | | Contributor(s):: Maude Cuchiara, NNCI Nano
In this lesson plan, students will be given several similar looking materials and asked to identify them by observing them at the macro and micro-scale. They will then be exposed to different analytical tools and describe how they can be used to explore materials at the nanoscale. ...
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Aerogels
05 Feb 2020 | | Contributor(s):: Carol Bouvier, James J Marti, Deb Newberry, Nano-Link Center for Nanotechnology Education
This module introduces aerogels, nicknamed “blue smoke.” Using exploratory activities this module introduces the nano structure of aerogels and the resulting extreme physical properties exhibited such as: thermal conductivity, transparency, and impact resistance. These properties...
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Introduction to Machine Learning in MSE: Predicting Bulk Modulus
29 Jan 2020 | | Contributor(s):: Adrian Nat Gentry, Peilin Liao
In this module, you will learn how to predict bulk modulus using machine learning.