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ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
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Active Learning via Bayesian Optimization for Materials Discovery
Online Presentations | 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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An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
Online Presentations | 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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The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Online Presentations | 25 Jun 2021 | Contributor(s):: Ryan Jacobs
This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction.
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Victor Wealth Adankai
https://nanohub.org/members/330788
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Classical Computing with Topological States: Coping with a post-Moore World
Online Presentations | 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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Parsimonious Neural Networks Learn Interpretable Physical Laws
Online Presentations | 21 Jun 2021 | Contributor(s):: Saaketh Desai
Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary...
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A Hands-on Introduction to Physics-Informed Neural Networks
Online Presentations | 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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Bayesian optimization tutorial using Jupyter notebook
Tools | 11 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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Batch Reification Fusion Optimization (BAREFOOT) Framework
Online Presentations | 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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SimTools: Delivering Simulations in the Era of Abundant Data
Online Presentations | 04 Jun 2021 | Contributor(s):: Alejandro Strachan
This presentation introduces SimTool, a library that allows developers to create, publish, and share reproducible workflows with well-defined and verified inputs and outputs.
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Gr-ResQ Tutorial II: Tool Demonstration and Training
Online Presentations | 04 Jun 2021 | Contributor(s):: Mitisha Surana
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Gr-ResQ (Graphene Rescue) Tool Tutorial & Training
Courses | 03 Jun 2021 | Contributor(s):: Mitisha Surana
This hands-on tutorial will introduce users to the Gr-ResQ (‘graphene rescue’) platform. Gr-ResQ is (i) an open, crowd-sourced database of recipes and characterization of graphene synthesized by chemical vapor deposition, (ii) a set of analysis tools that enable users to analyze the...
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Gr-ResQ Tutorial I: Introduction and Framework
Online Presentations | 03 Jun 2021 | Contributor(s):: Mitisha Surana
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Gr-ResQ Tutorial III: Machine Learning and Beyound
Online Presentations | 03 Jun 2021 | Contributor(s):: Mitisha Surana
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Anoop A Nair
I'm an integrated masters student in physics at the Indian Institute of Science Education and Research -Thiruvananthapuram
https://nanohub.org/members/328484
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May 26 2021
A Hands-on Introduction to Physics-Informed Neural Networks
Presenter:Ilias Bilionis, Purdue UniversityAbstract:Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential...
https://nanohub.org/events/details/1980
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IPython Notebooks for Machine Learning
Collections |
21 May 2021 |
Posted by Tanya Faltens
https://nanohub.org/members/29294/collections/ncn-ure
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A Hands-on Introduction to Physics-Informed Neural Networks
Tools | 21 May 2021 | Contributor(s):: Atharva Hans, Ilias Bilionis
A Hands-on Introduction to Physics-Informed Neural Networks
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FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities
Online Presentations | 20 May 2021 | Contributor(s):: Evan Reed