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Eugenio Culurciello
https://culurciello.github.iohttps://euge-blog.github.io
https://nanohub.org/members/403223
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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
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ANN Model Generator
11 Jul 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Simtool workflow to create ANN models for user datasets
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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....
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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...
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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
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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
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Machine Learning in Physics
04 Nov 2021 | | Contributor(s):: Nicolas Onofrio
Lectures and tutorials to learn how to write machine learning programs with Python
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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...
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MatSci 395 Lecture 5: MatCNN In-Class Tutorial
01 Nov 2021 | | Contributor(s):: Tiberiu Stan
Access MatCNN by clicking here
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MatSci 395 Lecture 6: How Do Convolutional Neural Networks Work?
01 Nov 2021 | | Contributor(s):: Tiberiu Stan
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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.
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MatSci 395 Lecture 4: Neural Network Training
07 Oct 2021 | | Contributor(s):: Tiberiu Stan
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MatSci 395 Lecture 3: How Do Neural Networks Work?
07 Oct 2021 | | Contributor(s):: Tiberiu Stan
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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
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Debugging Neural Networks
07 Aug 2021 | | Contributor(s):: Rishi P Gurnani
Debug common errors in neural networks.
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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...
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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...
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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
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Apr 23 2021
Parsimonious Neural Networks Learn Interpretable Physical Laws
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...
https://nanohub.org/events/details/1974