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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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Machine Learning Framework for Impurity Level Prediction in Semiconductors
15 Dec 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi
In this work, we perform screening of functional atomic impurities in Cd-chalcogenide semiconductors using high-throughput computations and machine learning.
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Feature Selection for Machine Learning
15 Dec 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan
Assessing feature selection for machine learning models
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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.
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Nov 11 2020
Machine Learning Framework for Impurity Level Prediction in Semiconductors workshop
Register now: https://purdue.webex.com/purdue/onstage/g.php?MTID=e088a6ccfa042d4ac13bdb4450fa3d14bSpeaker: Dr. Arun Mannodi, Argonne National LaboratoryThis series of workshops introduces...
https://nanohub.org/events/details/1875
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Only Physics can save Machine Learning!
13 Oct 2020 | | Contributor(s):: Muhammad A. Alam
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Hands-On Data Science and Machine Learning in Undergraduate Education
07 Oct 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai, Juan Carlos Verduzco Gastelum, Michael N Sakano, Zachary D McClure, Joseph M. Cychosz, Jared Gray West
This series of modules introduce key concepts in data science in the context of application in materials science and engineering.
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Neural Networks for Regression and Classification
01 Oct 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown great...
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Querying Materials Data Repositories
30 Sep 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan
This module introduces modern tools for data acquisition, including performing large queries using application programming interfaces (APIs), with hands-on online workflows.
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Active Learning for Design of Experiments
30 Sep 2020 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module introduces active learning in the context of materials discovery with hands-on online simulations.
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sayedul islam
https://nanohub.org/members/301108
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Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...
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nanoHUB: Online Simulation and Data
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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Ajjay S Gaadhe
https://nanohub.org/members/300022
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Probabilistic Computing: From Materials and Devices to Circuits and Systems
07 Sep 2020 | | Contributor(s):: Kerem Yunus Camsari
In this talk, I will describe one such path based on the concept of probabilistic or p-bits that can be scalably built with present-day technology used in magnetic memory devices.
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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.
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Ganesh Sri Sainath Chalamalasetti
Professional with 3 years of experience in Product Optimization Engineering. Highly skilled in Solidworks, and engineering process flow includes existing and new product development. Currently...
https://nanohub.org/members/296320
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ECG Data Analysis Using Machine Learning
03 Aug 2020 | | Contributor(s):: Rebecca Mosier, Guang Lin
Perform data analysis on ECG data using machine learning methods.
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Discovering discretized classical equations of motion using parsimonious neural networks
09 Jul 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan
Design and train neural networks in conjunction with genetic algorithms to discover classical equations of motion in a discretized form
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High Temperature Oxide Property Explorer
29 Jun 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan
Explore material properties of common and niche oxide materials for high-temperature applications