
Neural Networks for Regression and Classification
01 Oct 2020   Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with handson 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 great...

Linear Regression Models
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.

nanoHUB: Online Simulation and Data
24 Sep 2020   Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.

COVID19 data analysis
07 Aug 2020   Contributor(s):: Randy Heiland, Paul Macklin
Perform data analysis in a Jupyter notebook using data from the pc4covid19 tool.

ECG Data Analysis Using Machine Learning
03 Aug 2020   Contributor(s):: Rebecca Mosier, Guang Lin
Perform data analysis on ECG data using machine learning methods.

Interactive Learning Tools for Scientific Computing and Data Analysis Using R
29 Jul 2020   Contributor(s):: Cindy Nguyen, Rei SanchezArias
Rootfinding methods and numerical optimization techniques with applications in science, engineering, and data analysis

Data Analysis of Normal Data Sets in Engineering
24 Jul 2020   Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki
Statistical and data analysis concepts in engineering

Matlab Data Analysis Using Jupyter Notebooks
24 Jul 2020   Contributor(s):: Jon Nykiel, Anna Leichty, Zachary D McClure, Alejandro Strachan, Aileen Ryan, Adrian Nat Gentry, Amanda Johnston, Tamara Jo Moore, Allen Garner, Peter Bermel
Use Jupyter Notebooks with a Matlab kernel running in the background for data analysis and intro to engineering homework problems

Refractory Complex Concentrated Alloy Melting Point Calculation
28 May 2020   Contributor(s):: Zachary D McClure, Saaketh Desai, Alejandro Strachan
Calculate melting point of BCCtype high entropy alloys through phase coexistence method

Jon Nykiel
I'm Jon Nykiel, a fourth year undergraduate studying Materials Science and Applied Physics at the Ohio State University. I'm participating in NCN's SCALE URE program with Dr. Strachan of Purdue...
https://nanohub.org/members/288810

Test Tool for Neural Network Reactive Force Field for CHNO systems
14 May 2020   Contributor(s):: Pilsun Yoo, Saaketh Desai, Michael N Sakano, Peilin Liao, Alejandro Strachan
Run molecular dynamics and Do testing using the neural network reactive force field for nitramines

PhysiBoSSa: cell fate decision in TNF Boolean model
13 May 2020   Contributor(s):: Gerard Pradas, Arnau Montagud, Miguel Ponce de Leon
PhysiBoSSa model of the cell fate decision in TNF Boolean model in a multicellular multiscale system

XRD interactive trends plot
11 May 2020   Contributor(s):: Enze Chen
Observe changes in powder XRD spectra by modifying experimental parameters.

Running a Python 3 Script in a nanoHUB Jupyter Notebook
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...

Handson Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)
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...

Handson Supervised Learning: Part 2  Classification and Random Forests (2nd offering)
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...

Handson Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)
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...

Handson Supervised Learning: Part 2  Classification and Random Forests (1st offering)
24 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...

Handson Supervised Learning: Part 1  Linear Regression and Neural Networks
22 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces supervised learning via Jupyter notebooks on nanoHUB.org. You will learn how to setup a basic linear regression in a Jupyter notebook and then create and train a neural network. The tool used in this demonstration is Machine Learning for Materials Science:...

Repositories and Data Management (2nd offering)
22 Apr 2020   Contributor(s):: Zachary D McClure, Alejandro Strachan
This tutorial introduces database infrastructure and APIs for performing different scales of querying. You will learn how to access different suites of information from three prominent databases, and some advanced examples of data visualization and processing will be discussed. The Querying Data...