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3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction
04 Feb 2019 | | Contributor(s):: Elijah Reber
In this talk, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether...
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3 min Research Talk: Using Machine Learning for Materials Discovery and Property Prediction
26 Sep 2019 | | Contributor(s):: Mackinzie S Farnell
Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...
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3 min Research Talk: Web-based Machine Learning Tool for Material Discovery and Property Prediction
26 Sep 2019 | | Contributor(s):: Bryan Arciniega
This model allows the end-user to increase their knowledge on a scarce data set by using a data-rich property set. We also investigate the effect of chemical representation and autoencoder type on property prediction and compound generation.
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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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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...
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Active Learning via Bayesian Optimization for Materials Discovery
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.
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Adaptations to Convection Cells
21 Aug 2022 | | Contributor(s):: Chris Winkler, Rice University, NEWT Center
Changing temperature differences between the poles and the equator, and the rate of the Earth’s spin, create unique atmospheric patterns. These movements help to transfer heat from the equator to the poles thus creating weather. Deep Learning is used to help predict the changes due to...
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Advancing Photonic Device Design and Quantum Measurements with Machine Learning
18 Dec 2020 | | Contributor(s):: Alexandra Boltasseva
In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements.
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An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
25 Jun 2021 | | 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.
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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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ANN-based friction factor and Nusselt number models for developing flow across square pin fins
30 May 2023 | | Contributor(s):: Saeel Shrivallabh Pai, Justin A. Weibel
ANN-based correlations which provide friction factor and Nusselt number values for developing flows across square pin fins of different pitch.
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Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
14 Aug 2017 | | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan
Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...
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Autonomous Neutron Diffraction Experiments with ANDiE
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.
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Autonomous Neutron Diffraction Explorer
01 Nov 2021 | | Contributor(s):: Austin McDannald
Autonomously control neutron diffraction experiments to discover order parameter.
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Batch Reification Fusion Optimization (BAREFOOT) Framework
09 Jun 2021 | | Contributor(s):: Richard Couperthwaite
This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of...
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Bayesian optimization tutorial using Jupyter notebook
11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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Big Data in Reliability and Security: Applications
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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Big Data in Reliability and Security: Some Basics
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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Chemical Autoencoder for Latent Space Enrichment
19 Sep 2019 | | Contributor(s):: Bryan Arciniega, Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie
Chemical Autencoder uses machine learning for property prediction