Tags: machine learning

All Categories (1-20 of 195)

  1. Feb 23 2022

    nanoHUB Hands-On Workshop: Machine Learning with MATLAB

    Abstract: Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find...

    https://nanohub.org/events/details/2159

  2. Integrating Machine Learning with a Genetic Algorithm for Materials Exploration

    07 Dec 2021 | | Contributor(s):: Joseph D Kern

    In this talk, we will explore how this algorithm can be used for materials discovery.

  3. Feature Selection for CCA Strength Models

    06 Dec 2021 | | Contributor(s):: Zachary D McClure, Alejandro Strachan

    A tool to evaluate dataset of hardness and strength values of complex-concentrated-alloys. Feature selection, optimization, and explanation methods are included.

  4. 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.

  5. Polymer Genetic Algorithm

    05 Nov 2021 | | Contributor(s):: Joseph D Kern

    Generalized genetic algorithm designed for materials discovery.

  6. Machine Learning in Physics

    04 Nov 2021 | | Contributor(s):: Nicolas Onofrio

    Lectures and tutorials to learn how to write machine learning programs with Python

  7. MatSci 395 Laboratory: Computational Laboratory and Exercises

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

  8. Machine Learning in Materials Science: Image Analysis Using Convolutional Neural Networks in MatCNN

    03 Nov 2021 | | Contributor(s):: Tiberiu Stan, Jim James, Nathan Pruyne, Marcus Schwarting, Jiwon Yeom, Peter Voorhees, Ben J Blaiszik, Ian Foster, Jonathan D Emery

      This course introduces fundamental concepts of artificial intelligence within the context of materials science and image segmentation. The two-week module was taught as part of a Computational Methods in Materials Science course at Northwestern University. The module is aimed at...

  9. MatSci 395 Lecture 5: MatCNN In-Class Tutorial

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

    Access MatCNN by clicking here

  10. MatSci 395 Lecture 6: How Do Convolutional Neural Networks Work?

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

  11. Autonomous Neutron Diffraction Explorer

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

    Autonomously control neutron diffraction experiments to discover order parameter.

  12. Machine learning for high entropy atomic properties

    26 Oct 2021 | | Contributor(s):: Mackinzie S Farnell, Zachary D McClure, Alejandro Strachan

    Explore machine learning models used to assess the variations in local atomic properties in high entropy alloys.

  13. MatSci 395 Lecture 4: Neural Network Training

    07 Oct 2021 | | Contributor(s):: Tiberiu Stan

  14. MatSci 395 Lecture 3: How Do Neural Networks Work?

    07 Oct 2021 | | Contributor(s):: Tiberiu Stan

  15. MatSci 395 Lecture 1: Introduction to Machine Learning, Materials Imaging, and Segmentation

    29 Sep 2021 | | Contributor(s):: Tiberiu Stan

  16. 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...

  17. MATLAB R2021a

    09 Sep 2021 | | Contributor(s):: Steven Clark, Gen Sasaki, Lisa Kempler

    MATLAB is a programming and numeric computing platform to analyze data, develop algorithms, and create models.

  18. Debugging Neural Networks

    09 Sep 2021 | | Contributor(s):: Rishi P Gurnani

    The presentation will start with an overview of deep learning theory to motivate the logic in NetDebugger and end with a hands-on NetDebugger tutorial involving PyTorch, RDKit, and polymer data

  19. Debugging Neural Networks

    07 Aug 2021 | | Contributor(s):: Rishi P Gurnani

    Debug common errors in neural networks.

  20. ML-aided High-throughput screening for Novel Oxide Perovskite Discovery

    15 Jul 2021 | | Contributor(s):: Anjana Talapatra

    ML-based tool to discover novel oxide perovskites with wide band gaps