Tags: machine learning

All Categories (1-20 of 223)

  1. Abdul-Jabbar Bozdar

    I graduated majoring in electronics and currently working as a Computer Programmer. My keen interest in electronics engineering, semiconductor materials and devices brought me here.

    https://nanohub.org/members/387719

  2. 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.

  3. Nongnuch Artrith

    Dr. rer. nat. Nongnuch Artrith (http://nartrith.atomistic.net) is a Tenure-Track Assistant Professor in the Materials Chemistry and Catalysis group at the Debye Institute for Nanomaterials Science,...

    https://nanohub.org/members/384244

  4. No-code ML models

    18 Oct 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    No-code ML models

  5. 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...

  6. 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.

  7. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression

    26 Aug 2022 | | Contributor(s):: Davis McGregor

    This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.

  8. Adaptations to Convection Cells

    21 Aug 2022 | | Contributor(s):: Chris Winkler, Rice University, NEWT Center

    Changing temperature differences between the poles and the equator, and the rate of the Earth’s spin, create unique atmospheric patterns. These movements help to transfer heat from the equator to the poles thus creating weather. Deep Learning is used to help predict the changes due to...

  9. Detecting Cancerous Pollutants

    21 Aug 2022 | | Contributor(s):: Julia Dolive, Rice University, NEWT Center

    Developments in machine learning software and nanoparticles-assisted Surface-Enhanced Raman Scattering (SERS) techniques have remarkable potential in improving the detection accuracy and sensitivity of pollutants molecules. A cancerogenic class of environmental and biological pollutants of...

  10. SVR Machine Learning Workshop

    08 Aug 2022 | | Contributor(s):: Davis McGregor

    Introductory tutorial on support vector regression (SVR) machine learning, cross validation, and hyperparameter tuning.

  11. ANN Model Generator

    11 Jul 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    Simtool workflow to create ANN models for user datasets

  12. 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....

  13. MRS Computational Materials Science Tutorial

    04 May 2022 | | Contributor(s):: Panayotis Thalis Manganaris, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi

    Hands-on guide to the development of statistical models useful for materials design using python, sklearn, tensorflow, and intel extensions.

  14. Optical MNIST dataset

    21 Apr 2022 | | Contributor(s):: Hanyu Zheng

    Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in...

  15. Apr 13 2022

    Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop

    Abstract: 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...

    https://nanohub.org/events/details/2170

  16. Chemprop Demo

    11 Apr 2022 | | Contributor(s):: Kevin Greenman

    Demo of the Chemprop message-passing neural network package for the Hands-on Data Science and Machine Learning Training Series

  17. Wilson Eduardo Nieto

    https://nanohub.org/members/362314

  18. Machine Learning with MATLAB

    11 Mar 2022 | | Contributor(s):: Gaby Arellano Bello

    In this session, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.

  19. Ogoi, Nixon W

    https://nanohub.org/members/359135

  20. 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