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MatSci 395 Lecture 3: How Do Neural Networks Work?
Online Presentations | 07 Oct 2021 | Contributor(s):: Tiberiu Stan
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MatSci 395 Lecture 1: Introduction to Machine Learning, Materials Imaging, and Segmentation
Online Presentations | 29 Sep 2021 | Contributor(s):: Tiberiu Stan
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A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
Online Presentations | 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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Debugging Neural Networks
Online Presentations | 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
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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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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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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 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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FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities
Online Presentations | 20 May 2021 | Contributor(s):: Evan Reed
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FDNS21: Autonomous Research Systems for Carbon Nanotube Synthesis
Online Presentations | 20 May 2021 | Contributor(s):: Benji Maruyama
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FDNS21: Machine Learning Guided Synthesis of 2D Materials
Online Presentations | 27 Apr 2021 | Contributor(s):: Zheng Liu
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Convenient and efficient development of Machine Learning Interatomic Potentials
Online Presentations | 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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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
Online Presentations | 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.