-
Designing Machine Learning Surrogates for Molecular Dynamics Simulations
Online Presentations | 25 Nov 2021 | Contributor(s):: JCS Kadupitiya
Molecular dynamics (MD) simulations accelerated by high-performance computing (HPC) methods are powerful tools for investigating and extracting the microscopic mechanisms characterizing the properties of soft materials such as self-assembled nanoparticles, virus capsids, confined electrolytes,...
-
Autonomous Neutron Diffraction Experiments with ANDiE
Online Presentations | 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.
-
Polymer Genetic Algorithm
Tools | 05 Nov 2021 | Contributor(s):: Joseph D Kern
Generalized genetic algorithm designed for materials discovery.
-
Machine Learning in Physics
Tools | 04 Nov 2021 | Contributor(s):: Nicolas Onofrio
Lectures and tutorials to learn how to write machine learning programs with Python
-
MatSci 395 Laboratory: Computational Laboratory and Exercises
Teaching Materials | 03 Nov 2021 | Contributor(s):: Tiberiu Stan
-
Machine Learning in Materials Science: Image Analysis Using Convolutional Neural Networks in MatCNN
Courses | 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...
-
MatSci 395 Lecture 5: MatCNN In-Class Tutorial
Online Presentations | 01 Nov 2021 | Contributor(s):: Tiberiu Stan
Access MatCNN by clicking here
-
MatSci 395 Lecture 6: How Do Convolutional Neural Networks Work?
Online Presentations | 01 Nov 2021 | Contributor(s):: Tiberiu Stan
-
Autonomous Neutron Diffraction Explorer
Tools | 01 Nov 2021 | Contributor(s):: Austin McDannald
Autonomously control neutron diffraction experiments to discover order parameter.
-
Machine learning for high entropy atomic properties
Tools | 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.
-
MatSci 395 Lecture 4: Neural Network Training
Online Presentations | 07 Oct 2021 | Contributor(s):: Tiberiu Stan
-
MatSci 395 Lecture 3: How Do Neural Networks Work?
Online Presentations | 07 Oct 2021 | Contributor(s):: Tiberiu Stan
-
MatSci 395 Lecture 1: Introduction to Machine Learning, Materials Imaging, and Segmentation
Online Presentations | 29 Sep 2021 | Contributor(s):: Tiberiu Stan
-
Nathaniel Curran
https://nanohub.org/members/339989
-
A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
Online Presentations | 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...
-
MATLAB R2021a
Tools | 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.
-
Debugging Neural Networks
Online Presentations | 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
-
Debugging Neural Networks
Tools | 07 Aug 2021 | Contributor(s):: Rishi P Gurnani
Debug common errors in neural networks.
-
ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
-
Active Learning via Bayesian Optimization for Materials Discovery
Online Presentations | 25 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal
In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application.