Resources (1-20 of 38)

1. A Guided Tour of Interactive Jupyter Notebooks Powered by nanoHUB

   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...

2. Micromagnetic Simulation of Magnetic Nanowires (MNW) using OOMMF to Predict Heating Ability

   11 Jan 2023 | | Contributor(s):: Yicong Chen

   Using the OOMMF simulation tool in nanoHUB, we will learn how to specify properties of the MNWs, such as their geometry and material and how these properties will affect their magnetic reversal behavior. For instance, we will use the graph function in OOMMF to observe the changes of hysteresis...

3. Gaussian Process Regression for Surface Interpolation

   22 Nov 2022 | | Contributor(s):: Zhiqiao Dong, Manan Mehta

   This tutorial will introduce the fundamentals of GPR and its application to surface interpolation. We will also introduce a new technique called filtered kriging (FK), which uses a pre-filter to improve interpolation performance.

4. Teaching Electronic Structure Methods in Chemistry Using Simulation Tools in nanoHUB

   13 Oct 2022 | | Contributor(s):: Nicole Adelstein

   Participants will get hands-on practice with lessons on Hartree-Fock and basis sets using the nanoHUB tool ORCA and the opportunity to ask questions about teaching with nanoHUB.

5. Microfluidics: Hands-On Computational Examples

   12 Oct 2022 | | Contributor(s):: Atilla Ozgur Cakmak, NACK Network

   Simulation of a microfluidic channel to separate red blood cells will also be covered as an immediate application.

6. The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction

   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...

7. Hands-on Teaching with Jupyter Notebooks on nanoHUB

   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.

8. Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties

   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.

9. Integrating Microelectronics Contexts into Engineering Classrooms: Thermo-Calc Online Tool for the Design of Solder Materials

   20 May 2022 | | Contributor(s):: Congying Wang

   This presentation will first present how industrial soldering practices can be contextualized into current engineering classrooms, especially in materials science, to provide students with situated learning experiences. Then we will demonstrate how Thermo-Calc can be utilized as an effective...

10. Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop

    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....

11. Integrated Computational Materials Engineering in the Classroom: Teaching Fundamental Thermodynamics and Kinetics Through an Industry Focused Lens

    07 Jan 2022 | | Contributor(s):: Adam Hope

    This presentation will demonstrate ways that Thermo-Calc can be used in the classroom, as a teaching tool, and in industry, as a research and problem solving tool for any ICME framework.

12. Autonomous Neutron Diffraction Experiments with ANDiE

    14 Nov 2021 | | Contributor(s):: Austin McDannald

    This tutorial will cover the working principles of ANDiE, how physics was encoded into the design, and demonstrate how ANDiE can be used to autonomously control neutron diffraction experiments.

13. MatSci 395 Lecture 2.2: Segmentation Using GIMP

    01 Nov 2021 | | Contributor(s):: Tiberiu Stan

    In this hands-on exercise we will manually segment an image from the XCT dataset of Al-Zn solidification using GIMPThe example image (c54_ObjT1_z164.png) for this exercise can be downloaded here:Example Image for Segmentation: 852x852 pixel XCT image

14. Get your feet wet! An Introduction to the nanoHUB Simulation Environment

    29 Oct 2021 | | Contributor(s):: Tanya Faltens, The Micro Nano Technology - Education Center

    This presentation will demonstrate some nanoHUB functionalities for educators and showcase a few interactive Jupyter notebooks, simulation tools and educational modules.

15. A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery

    09 Sep 2021 | | Contributor(s):: Anjana Talapatra

    In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator...

16. 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.

17. 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.

18. 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.

19. 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...

20. 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.