Tags: data science

All Categories (1-20 of 53)

  1. 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

  2. Learning and Teaching Data Science using nanoHUB’s Cloud Resources

    18 Mar 2022 | | Contributor(s):: Alejandro Strachan

    This talk will discuss how data science is accelerating innovation in STEM fields. These tools enable the efficient handling of valuable data, the identification of patterns in large data collections, the development of predictive models, and the optimal design of experiments.

  3. 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.

  4. Data Analysis with MATLAB

    04 Mar 2022 | | Contributor(s):: Gen Sasaki

    Learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your coursework as well as in research.

  5. MATLAB R2021a

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

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

  6. Introduction to dplyr, ggplot2 and Other tidyverse Friends: Modern Tools for Data Exploration and Visualization

    08 Jul 2021 | | Contributor(s):: Rei Sanchez-Arias

    In recent years, interest in the development of predictive models and the use of machine learning libraries has grown rapidly. As part of the efficient implementation of different models, a fundamental component of this process deals with data preparation and cleaning, followed by exploration,...

  7. Utilizing Modern Data Exploration and Visualization Tools for STEM Applications and Datasets

    08 Jul 2021 | | Contributor(s):: Rei Sanchez-Arias

    If you're an instructor in a STEM field who wants to add a data science component to an existing course, this series will give you the tools. You'll leave these sessions with practical knowledge that will empower your students.Topics include:Data preparation and cleaningData...

  8. 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.

  9. tidyverse Data Science Tools for STEM Applications and Datasets

    25 Jun 2021 | | Contributor(s):: Rei Sanchez-Arias

    An introduction to dplyr, ggplot2, and other tidyverse data science tools in STEM applications

  10. Victor Wealth Adankai

    https://nanohub.org/members/330788

  11. Sheimy Paz Serpa

    https://nanohub.org/members/330719

  12. A Hands-on Introduction to Physics-Informed Neural Networks

    16 Jun 2021 | | Contributor(s):: Ilias Bilionis, Atharva Hans

    Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural...

  13. A Hands-on Introduction to Physics-Informed Neural Networks

    21 May 2021 | | Contributor(s):: Atharva Hans, Ilias Bilionis

    A Hands-on Introduction to Physics-Informed Neural Networks

  14. Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial

    07 May 2021 | | Contributor(s):: Ryan Jacobs, BENJAMIN AFFLERBACH

    Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.

  15. DFT Results Explorer

    17 Feb 2021 | | Contributor(s):: Saaketh Desai, Juan Carlos Verduzco Gastelum, Daniel Mejia, Alejandro Strachan

    Use visualization tools to explore correlations in a DFT simulation results database

  16. Module 1: Making Data Accessible, Discoverable and Useful

    27 Jan 2021 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum

    This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is...

  17. Module 3: Materials Descriptors for Data Science

    27 Jan 2021 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum, Zachary D McClure

    This module focuses on the use of descriptors to improve the description of materials in machine learning. Augmenting input parameters with appropriate descriptors (a process sometimes called featurization) can often significantly improve the accuracy of predictive models. Ideal descriptors are...

  18. Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders

    13 Nov 2020 | | Contributor(s):: Vinay Hegde, Alejandro Strachan

    This tutorial introduces deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint.

  19. Oct 21 2020

    Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders workshop

    Registration for this event is now closed. Thanks for your interest!This series of workshops introduces participants to important concepts and techniques in data science and machine learning in the...

    https://nanohub.org/events/details/1869

  20. Only Physics can save Machine Learning!

    13 Oct 2020 | | Contributor(s):: Muhammad A. Alam