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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 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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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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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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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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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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Classical Computing with Topological States: Coping with a post-Moore World
21 Jun 2021 | | Contributor(s):: Avik Ghosh
There are two examples I will focus on ? one is doing conventional Boolean logic at low power below the thermal Boltzmann limit, using the topological properties of Dirac fermions to control transmission across a gated interface. The other is doing collective computing using temporal state...
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
09 Mar 2021 | | Contributor(s):: Chi Chen
This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional operations are carried out on the materials graphs.
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Convenient and efficient development of Machine Learning Interatomic Potentials
09 Mar 2021 | | Contributor(s):: Yunxing Zuo
This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models.
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Creating Inflections: DARPA’s Electronics Resurgence Initiative
09 Jan 2019 | | Contributor(s):: William Chappell
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Data Science and Machine Learning for Materials Science
22 Jan 2020 | | Contributor(s):: Saaketh Desai
This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...
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Data-Driven Discovery of Governing Equations of Physical Systems
16 Jan 2019 | | Contributor(s):: J. Nathan Kutz
We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete...
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Data-Driven Materials Innovation: where Machine Learning Meets Physics
29 Nov 2023 | | Contributor(s):: Anand Chandrasekaran
Learn how Schrödinger’s tools can address common issues by using a combination of physics-based simulation data, enterprise informatics, and chemistry-aware ML.
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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