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Carbon NanoTubes: Structure - Properties - Applications
19 Mar 2012 | | Contributor(s):: Yuri A Kruglyak
Presentation slides for seminar given for students of Faculty of Computer Sciences of Odessa State Environmental University, Ukraine by Prof. Yuri Kruglyak on May 22, 2008.
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CELL-MET Thrust Area 2: Nanomechanics: Mechanical Properties of Materials, Scaffolds and Cardiac Tissues
04 Apr 2019 | | Contributor(s):: Pranjal Nautiyal
In this presentation, FIU graduate student Pranjal Nautiyal explains his work on Nanomechanics in Arvind Agarwal's Lab.
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Chemical and Physical Properties of Endohedrally Doped Nanodiamonds
09 Nov 2022 | | Contributor(s):: Tomekia Simeon
The semiempirical electronic structure Parametric Method 3 (PM3) at the nanoHUB.org website is introduced to the student in this assignment. In particular, this semiempirical method is applied to study dopant semiconductor materials intercalated in two types of nanodiamond (ND) complexes:...
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Chemical Autoencoder for Latent Space Enrichment
19 Sep 2019 | | Contributor(s):: Bryan Arciniega, Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie
Chemical Autencoder uses machine learning for property prediction
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CHM 696 Lecture 14: Semiconductor Nanoparticles, Nanorods, and Nanowires: Properties and Applications I
02 Jun 2011 | | Contributor(s):: Alexander Wei
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CHM 696 Lecture 15: Semiconductor Nanoparticles, Nanorods, and Nanowires: Properties and Applications II
02 Jun 2011 | | Contributor(s):: Alexander Wei
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Complex Oxides and the Philosopher’s Stone
26 May 2016 | | Contributor(s):: Shiram Ramanathan
In this talk I will discuss the topic of insulator-metal transitions in strongly correlated oxides, their control via disorder, orbital occupancy and electric fields and challenges these systems pose to our contemporary understanding of emergent phenomena in ionic lattices.
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Computational Nanoscience, Lecture 3: Computing Physical Properties
11 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin
In this lecture, we'll cover how to choose initial conditions, and how to compute a number of important physical observables from the MD simulation. For example, temperature, pressure, diffusion coefficient, and pair distribution function will be highlighted. We will also discuss briefly the...
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Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin
In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly...
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
26 Jan 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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Convenient and efficient development of Machine Learning Interatomic Potentials
26 Jan 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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Dancing Magnets - Ferrofluids Lab
28 Dec 2016 | | Contributor(s):: SHINE Project, SHINE Project
This lab consists of activities designed to teach students about ferrofluids and how nanotechnology can be used to modify the properties of magnetic materials. The entire bundle can be downloaded by clicking on the download link in the upper right or compontents can be individually...
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Data Science and Machine Learning for Materials Science
22 Jan 2020 | | Contributor(s):: Saaketh Desai
This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...
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Designing Machine Learning Surrogates for Molecular Dynamics Simulations
25 Nov 2021 | | Contributor(s):: JCS Kadupitiya
Molecular dynamics (MD) simulations accelerated by high-performance computing (HPC) methods are powerful tools for investigating and extracting the microscopic mechanisms characterizing the properties of soft materials such as self-assembled nanoparticles, virus capsids, confined electrolytes,...
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DFT Material Properties Simulator
21 Jul 2015 | | Contributor(s):: Gustavo Javier, Usama Kamran, David M Guzman, Alejandro Strachan, Peilin Liao, Robert Joseph Appleton
Compute electronic and mechanical properties of materials from DFT calculations with 1-Click
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Direct mechanical measurement of the tensile strength and elastic modulus of multiwalled carbon nanotubes
07 Oct 2011 | | Contributor(s):: Brian Demczyk, Y.M. Wang, J. Cumings, M. Hetman, W. Han, A. Zettl. R. O. Ritchie
This work represents the first in-situ measurenment of the tensile strength of a carbon nanotuube.
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Dynamic Crack Growth under Periodic Excitation Simulator
02 Aug 2017 | | Contributor(s):: Rachel Katherine Kohler, Nicolò Grilli, Marisol Koslowski
Simulate 2D crack growth due to sinusoidal loading.
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Effective Integration of NIST Reference Data, Reference Materials, and Informatics in Support of Science and Technology
15 May 2019 | | Contributor(s):: Carlos A. Gonzalez
In this talk, a general description of NIST’s SRM program will be provided, highlighting some examples related to environmental science, clinical diagnoses and petroleum chemistry. In addition, issues related to the effective integration of reference data with reference materials and...
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Electric and Magnetic Properties of Multiferroic Oxide Thin Films and Heterostructures
20 Oct 2010 | | Contributor(s):: Pedro Antonio Prieto
Outline:IntroductionPreparation methods for oxide thin filmsOxide thin films and heterostructures Multiferroic materialsBiFeO3, YMnO3, BiMnO3 thin films and FE/FM CompositesConclusions
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Electronic Characterization of Materials Using Conductive AFM
21 Mar 2011 | | Contributor(s):: Amir Moshar