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Aytekin Gel
https://nanohub.org/members/327168
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Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial
Tools | 07 May 2021 | Contributor(s):: Ryan Jacobs, BENJAMIN AFFLERBACH
Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.
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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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Apr 23 2021
Parsimonious Neural Networks Learn Interpretable Physical Laws
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...
https://nanohub.org/events/details/1974
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Qazi Shahid Ullah
https://nanohub.org/members/322959
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Mar 30 2021
MNT-EC Spring Development Workshop: CVD Synthesis and Image Analysis
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...
https://nanohub.org/events/details/1966
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Mar 23 2021
MNT-EC Spring Development Workshop: CVD Synthesis and Image Analysis
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...
https://nanohub.org/events/details/1965
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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.
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Wisdom Mawuenyefia Amenyo
Mawuenyefia Wisdom Amenyo is a rising senior at Kwame Nkrumah University of Science and Technology, Ghana. With majors in Biomedical Engineering alongside a MedTech and AI enthusiast, he's working...
https://nanohub.org/members/316828
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Feb 03 2021
Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
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...
https://nanohub.org/events/details/1953
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U-Net Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
Online Presentations | 01 Feb 2021 | Contributor(s):: Aagam Rajeev Shah
This tutorial introduces you to U-Net, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a U-Net neural network, and apply it to segment scanning electron microscopy images of...
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Materials Graph Network
Tools | 27 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo
Materials Graph Networks for molecule and crystal structure-property relationship modeling
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Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
Online Presentations | 27 Jan 2021 | Contributor(s):: Aagam Rajeev Shah
This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a...
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Module 1: Making Data Accessible, Discoverable and Useful
Online Presentations | 27 Jan 2021 | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is...
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Module 3: Materials Descriptors for Data Science
Online Presentations | 27 Jan 2021 | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum, Zachary D McClure
This module focuses on the use of descriptors to improve the description of materials in machine learning. Augmenting input parameters with appropriate descriptors (a process sometimes called featurization) can often significantly improve the accuracy of predictive models. Ideal descriptors are...
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Machine Learning Force Field for Materials
Tools | 25 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo
Machine learning force field for materials
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SEM Image Segmentation Workshop
Tools | 12 Jan 2021 | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin
This tool introduces users to machine learning used to segment microscopy images
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Advancing Photonic Device Design and Quantum Measurements with Machine Learning
Online Presentations | 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
Online Presentations | 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.