-
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...
-
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.
-
A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
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...
-
ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
15 Jul 2021 | | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
-
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
-
FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities
20 May 2021 | | Contributor(s):: Evan Reed
-
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...
-
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.
-
Uncertainty Quantification and Scientific Machine Learning for Complex Engineering Systems
17 Aug 2020 | | Contributor(s):: Guang Lin
In this talk, I will first present a review of the novel UQ techniques I developed to conduct stochastic simulations for very large-scale complex systems.
-
Hands-on Deep Learning for Materials
10 Jun 2020 | | Contributor(s):: Saaketh Desai, Edward Kim, Vinay Hegde
This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint
-
Synthesis of Graphene by Chemical Vapor Deposition Part II: Data Science + Graphene Synthesis
29 Apr 2020 | | Contributor(s):: Sameh H Tawfick
Overall, these two lectures are meant to be a general introduction on the opportunities and challenges related to graphene synthesis.
-
Hands-on Data Science and Machine Learning Training Series
21 Apr 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi
his series of workshops introduces participants to important concepts and techniques in data science and machine learning in the context engineering and physical sciences applications. All workshops include hands-on activities.
-
Infrastructure for Data-Driven Discovery: Materials Data Facility and DLHub
15 Apr 2019 | | Contributor(s):: Ian Foster