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.
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.
Feature Selection for Machine Learning
15 Dec 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan
Assessing feature selection for machine learning models
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.
Nov 11 2020
Machine Learning Framework for Impurity Level Prediction in Semiconductors workshop
Only Physics can save Machine Learning!
13 Oct 2020 | | Contributor(s):: Muhammad A. Alam
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.
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...
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.
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.
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. ...
nanoHUB: Online Simulation and Data
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
Ajjay S Gaadhe
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.
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.
Ganesh Sri Sainath Chalamalasetti
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.
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
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