All Categories (1-20 of 206)

1. Scientific Data Visualization using Python

   09 Jun 2022 | | Contributor(s):: Jessica Nash, Ashley Ringer McDonald

   This lecture looks at scientific data visualization using matplotlib, plotly, and visulizing molecular structures using scientific NGLView.

2. Tutorial demonstration problem

   Q&A|Closed | Responses: 1

   Hi, I'm new to NEMO5 https://nanohub.org/answers/question/2508

3. Active Learning via Bayesian Optimization for Materials Discovery

   25 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal

   In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application.

4. 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.

5. 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.

6. 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...

7. Bayesian optimization tutorial using Jupyter notebook

   11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal

   Active learning via Bayesian optimization for materials discovery

8. S4 Tutorial P1: Overview and Example 1 - Plane Wave Incident on Air-Glass Interface

   08 Apr 2021 | | Contributor(s):: Jie Zhu, Enas Sakr, Peter Bermel

   This presentation is part of the three part tutorial for the S4 tool (Stanford Stratified Structure Solver) on nanoHUB designed for the nanoHUB IGNITE challenge. In the tutorial, we give an overview of the S4 electromagnetic simulation tool, and demonstrate the basic features through three...

9. 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.

10. 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.

11. U-Net Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene

    01 Feb 2021 | | Contributor(s):: Aagam Rajeev Shah

    This tutorial introduces you to U-Net, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a U-Net neural network, and apply it to segment scanning electron microscopy images of...

12. Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene

    27 Jan 2021 | | Contributor(s):: Aagam Rajeev Shah

    This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a...

13. 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.

14. 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...

15. Setting up Your nanoHUB File Structure in Jupyter Notebooks

    17 Apr 2020 | | Contributor(s):: Tanya Faltens

    This tutorial takes you through the steps to set up your nanoHUB file structure in Jupyter Notebooks.Be sure to get a copy of the pdf that accompanies the video instructions by clicking on the Supporting Docs tab for this resource.

16. Mini Course on Physical Models

    04 Sep 2019 | | Contributor(s):: Bruno Uchoa

    This mini-course of four lectures is intended to stimulate undergraduate students to think critically about the the modeling process in physics.

17. SMART Films Tutorials

    05 Jun 2019 | | Contributor(s):: Ali Shakouri (organizer)

18. Uncertainty Quantification Tutorial using Jupyter Notebooks

    02 Oct 2018 | | Contributor(s):: Ilias Bilionis

    Increasing modeling detail is not necessarily correlated with increasing predictive ability. Setting modeling and numerical discretization errors aside, the more detailed a model gets, the larger the number of parameters required to accurately specify its initial/boundary conditions,...

19. MATLAB Tutorial

    Collections | 13 Jul 2018 | Posted by Jesse Lee Hoffman

    https://nanohub.org/groups/ncnure2018/collections/ncn-ure-2018---good-nanohub-resources

20. Introduction to Molecular Dynamics Showcase

    13 Feb 2017 | | Contributor(s):: Chen-Yu Li, Karl Steven Decker (editor), Aleksei Aksimentiev

    In this tutorial, we will demonstrate how to use the MD showcase builder tool to create a showcase. We will start from the simplest example – creating a showcase from a PDB file – and move on to more complicated examples. We will also cover how to add a description, change...