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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
Online Presentations | 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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Machine Learning Defect Behavior in Semiconductors
Tools | 10 Nov 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)
Develop machine learning models to predict defect formation energies in chalcogenides
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Bakhtiyor Rasulev
https://nanohub.org/members/305866
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Only Physics can save Machine Learning!
Online Presentations | 13 Oct 2020 | Contributor(s):: Muhammad A. Alam
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Hands-On Data Science and Machine Learning in Undergraduate Education
Courses | 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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Module 5: Neural Networks for Regression and Classification
Online Presentations | 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...
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Module 2: Querying Materials Data Repositories
Online Presentations | 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. Cyber-infrastructure platforms for data offer unparalleled access to data, this module will introduce tools to manage,...
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Module 7: Active Learning for Design of Experiments
Online Presentations | 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. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore...
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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
Presentation Materials | 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
Presentation Materials | 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
Online Presentations | 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
Online Presentations | 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
Tools | 03 Aug 2020 | Contributor(s):: Rebecca Mosier, Guang Lin
Perform data analysis on ECG data using machine learning methods.
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Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride
Tools | 03 Aug 2020 | Contributor(s):: Saswat Mishra, Karthik Guda Vishnu, Alejandro Strachan
Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride as a function of Strain and Aluminum doping
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Parsimonious neural networks
Tools | 09 Jul 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan
Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data