-
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
-
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.
-
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.
-
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.
-
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.
-
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,...
-
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...
-
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.
-
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
-
Victor Wealth Adankai
https://nanohub.org/members/330788
-
Sheimy Paz Serpa
https://nanohub.org/members/330719
-
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...
-
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
-
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.
-
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
-
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...
-
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...
-
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.
-
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
-
Only Physics can save Machine Learning!
13 Oct 2020 | | Contributor(s):: Muhammad A. Alam