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Exploring the Nano World: Building Nanoscale Structures with Polymer Modeler
Online Presentations | 14 Jul 2023 | Contributor(s):: Tongtong Shen
In this talk, I will showcase how atomic-level simulations can lead to a more fundamental understanding of PAN crystal structures and guide you through an interactive Polymer Modeler powered by nanoHUB.
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Ufoma Silas Anamu
https://nanohub.org/members/407195
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Raphael N Ogbodo
https://nanohub.org/members/404680
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Jun 09 2023
Exploring the Nano World: Building Nanoscale Structures with Polymer Modeler
Exploring the Nano World: Building Nanoscale Structures with Polymer ModelerPresenter:Dr. Tongtong (Tanya) Shen, AppleDate and Time:June 9, 2023; 2:00 - 3:00 PM EDTRegister hereAbstract:The...
https://nanohub.org/events/details/2263
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A Condensed Matter Physics class and a Course-Based Undergraduate Research Experience (CURE) with the MIT Atomic-Scale Modeling Toolkit
Online Presentations | 07 Nov 2022 | Contributor(s):: David Strubbe
In this presentation, Dr. Strubbe will discuss how he has been using the MIT Atomic-Scale Modeling Toolkit as a part of his undergraduate and graduate class on condensed matter physics. In discussion sections, simulations are performed to illustrate concepts like covalent bonding,...
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Density Functional Theory: Introduction and Applications
Online Presentations | 07 Nov 2022 | Contributor(s):: André Schleife
In this webinar, Dr. Schleife will briefly outline the fundamentals of DFT, and demonstrate how to use Quantum Espresso in nanoHUB to compute electronic structure, electronic densities of state, total energies, and bulk modulus for example materials.
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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
Online Presentations | 06 Oct 2022 | Contributor(s):: Ryan Jacobs
Hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of...
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Back to School Webinar Series on Teaching with nanoHUB
Series | 06 Oct 2022
These webinars are opportunities to learn from faculty who use nanoHUB in their classes and research. The webinars will generally focus on using nanoHUB simulation apps and assignments or aspects of teaching or doing research using nanoHUB resources.
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Introduction to DFT simulations in nanoHUB
Presentation Materials | 06 Oct 2022 | Contributor(s):: André Schleife
In this webinar, I will briefly outline the fundamentals of this technique, and demonstrate applications to compute total energies, bulk modulus, and electronic structure/densities of states using Nanohub.
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Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties
Online Presentations | 04 Oct 2022 | 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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Saeed Norouzi
https://nanohub.org/members/364936
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Reitesh KV Raman
https://nanohub.org/members/350801
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Machine Learning in Physics
Tools | 04 Nov 2021 | Contributor(s):: Nicolas Onofrio
Lectures and tutorials to learn how to write machine learning programs with Python
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Andrés Felipe Sierra
BSc Chemical Engineering & Data Scientist. Materials Science and Nanotech enthusiasm.
https://nanohub.org/members/344047
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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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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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Krishna Sai Kaligotla
https://nanohub.org/members/308153
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Machine Learning Defect Behavior in Semiconductors
Tools | 09 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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Jeffrey W Bullard
https://nanohub.org/members/295876
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Data Analysis of Normal Data Sets in Engineering
Tools | 04 Jun 2020 | Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki
Statistical and data analysis concepts in engineering