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Developing Software Tools in Computational Biology
Online Presentations | 15 Dec 2023 | Contributor(s):: T.J. Sego, The Micro Nano Technology - Education Center
This talk presents the CompuCell3D and Tissue Forge modeling and simulation environments. The scope, basic capabilities, and select features of both software packages are surveyed. ...
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NanoHUB Jupyter Notebooks for Research and Education
Online Presentations | 15 Jun 2023 | Contributor(s):: Michael Earl Reppert
Prof. Reppert presents and over view of using nanoHUB Jupyter Notebooks to create applications for teaching chemistry. Also presented is a summary of the notebooks developed by his group.
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Blockchain and Proof of Work Lab
Online Presentations | 25 Apr 2023 | Contributor(s):: Oneal Douglin, Yu-Chung Chang, Alfonso Daniel Meraz, Brian Hyun-jong Lee, Shivam Tripathi, Alejandro Strachan
Blockchain is a technology for a distributed, tamperproof ledger, with untrusted agents.
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A Guided Tour of Interactive Jupyter Notebooks Powered by nanoHUB
Online Presentations | 20 Feb 2023 | Contributor(s):: Daniel Mejia
In this presentation, we will take you on a guided tour of interactive Jupyter Notebooks powered by nanoHUB. Jupyter is a powerful tool for data science and scientific computing that provides an intuitive interface for a variety of programming languages; Jupyter in nanoHUB provides even more...
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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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Hands-on Teaching with Jupyter Notebooks on nanoHUB
Online Presentations | 04 Oct 2022 | Contributor(s):: Michael Earl Reppert
Dr. Reppert will discuss his use of nanoHUB Jupyter Notebook-based content in college Chemistry courses, focusing on nanoHUB's unique possibilities for hands-on simulation, visualization, and programming projects.
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Blockchain & Proof of Useful Work
Online Presentations | 01 Jul 2022 | Contributor(s):: Alejandro Strachan, Oneal Douglin, Yu-Chung Chang, Alfonso Daniel Meraz, Brian Hyun-jong Lee, Shivam Tripathi, The Micro Nano Technology - Education Center
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Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop
Online Presentations | 06 May 2022 | Contributor(s):: Kevin Greenman
Chemprop is an open-source implementation of a directed message passing neural network (D-MPNN) that has been demonstrated to be successful in predicting a variety of molecular properties, including solvation properties, optical properties, infrared spectra, and toxicity....
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How to clean up your nanoHUB file space
Online Presentations | 20 Apr 2022 | Contributor(s):: Tanya Faltens
This video tutorial demonstrates three ways of cleaning up your nanoHUB filespace, using your sessions module, or using the Jupyter dashboard or terminal.
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Integrating Machine Learning with a Genetic Algorithm for Materials Exploration
Online Presentations | 07 Dec 2021 | Contributor(s):: Joseph D Kern
In this talk, we will explore how this algorithm can be used for materials discovery.
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Gateways 2021 Tutorial: SimTools—Software tools that are FAIR
Online Presentations | 07 Dec 2021 | Contributor(s):: Steven Clark, Daniel Mejia, Alejandro Strachan
In this tutorial, we will walk attendees through how SimTools operate within the nanoHUB.org gateway. In a 90-minute session designed for beginners, attendees will create an account on nanoHUB to complete hands-on activities during the tutorial. Some experience with Python is preferred.
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Incorporating Notebooks in STEM Classes and Deploying Interactive Applications
Online Presentations | 10 Aug 2021 | Contributor(s):: Rei Sanchez-Arias
In this talk we discuss how notebooks can be introduced as working examples for hands-on classwork activities in different courses. We also briefly discuss the development and deployment of interactive applications useful for data exploration, analysis, and reporting, that users can host as...
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Teaching Engineering using Jupyter Notebooks
Online Presentations | 29 Dec 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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Jupyter in nanoHUB: Developing and Deploying Jupyter Tools in nanoHUB
Online Presentations | 16 Dec 2020 | Contributor(s):: Alejandro Strachan
This presentation is available for pre-screening. The final presentation production will be forth coming.
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Module 5: Neural Networks for Regression and Classification
Online Presentations | 01 Oct 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown...
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Module 4: Linear Regression Models
Online Presentations | 01 Oct 2020 | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine...
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Running a Python 3 Script in a nanoHUB Jupyter Notebook
Online Presentations | 01 May 2020 | Contributor(s):: Tanya Faltens
This tutorial will show you how to create and run Python 3 code in a Jupyter notebook, rather than creating and running a Python script. We are working along with Chapter 1.8 “Writing a program” in the Python for Everybody course. In this lesson they execute a Python script that...
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)
Online Presentations | 30 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (2nd offering)
Online Presentations | 30 Apr 2020 | Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)
Online Presentations | 29 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...