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Abdul-Jabbar Bozdar
I graduated majoring in electronics and currently working as a Computer Programmer. My keen interest in electronics engineering, semiconductor materials and devices brought me here.
https://nanohub.org/members/387719
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Gaussian Process Regression for Surface Interpolation
22 Nov 2022 | | Contributor(s):: Zhiqiao Dong, Manan Mehta
This tutorial will introduce the fundamentals of GPR and its application to surface interpolation. We will also introduce a new technique called filtered kriging (FK), which uses a pre-filter to improve interpolation performance.
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Nongnuch Artrith
Dr. rer. nat. Nongnuch Artrith (http://nartrith.atomistic.net) is a Tenure-Track Assistant Professor in the Materials Chemistry and Catalysis group at the Debye Institute for Nanomaterials Science,...
https://nanohub.org/members/384244
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No-code ML models
18 Oct 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
No-code ML models
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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
06 Oct 2022 | | Contributor(s):: Ryan Jacobs
Hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of...
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Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties
04 Oct 2022 | | 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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Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression
26 Aug 2022 | | Contributor(s):: Davis McGregor
This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.
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Adaptations to Convection Cells
21 Aug 2022 | | Contributor(s):: Chris Winkler, Rice University, NEWT Center
Changing temperature differences between the poles and the equator, and the rate of the Earth’s spin, create unique atmospheric patterns. These movements help to transfer heat from the equator to the poles thus creating weather. Deep Learning is used to help predict the changes due to...
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Detecting Cancerous Pollutants
21 Aug 2022 | | Contributor(s):: Julia Dolive, Rice University, NEWT Center
Developments in machine learning software and nanoparticles-assisted Surface-Enhanced Raman Scattering (SERS) techniques have remarkable potential in improving the detection accuracy and sensitivity of pollutants molecules. A cancerogenic class of environmental and biological pollutants of...
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SVR Machine Learning Workshop
08 Aug 2022 | | Contributor(s):: Davis McGregor
Introductory tutorial on support vector regression (SVR) machine learning, cross validation, and hyperparameter tuning.
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ANN Model Generator
11 Jul 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Simtool workflow to create ANN models for user datasets
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Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop
06 May 2022 | | Contributor(s):: Kevin Greenman
Chemprop is an open-source implementation of a directed message passing neural network (D-MPNN) that has been demonstrated to be successful in predicting a variety of molecular properties, including solvation properties, optical properties, infrared spectra, and toxicity....
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MRS Computational Materials Science Tutorial
04 May 2022 | | Contributor(s):: Panayotis Thalis Manganaris, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi
Hands-on guide to the development of statistical models useful for materials design using python, sklearn, tensorflow, and intel extensions.
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Optical MNIST dataset
21 Apr 2022 | | Contributor(s):: Hanyu Zheng
Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in...
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Apr 13 2022
Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop
Abstract: Chemprop is an open-source implementation of a directed message passing neural network (D-MPNN) that has been demonstrated to be successful in predicting a variety of molecular...
https://nanohub.org/events/details/2170
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Chemprop Demo
11 Apr 2022 | | Contributor(s):: Kevin Greenman
Demo of the Chemprop message-passing neural network package for the Hands-on Data Science and Machine Learning Training Series
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Wilson Eduardo Nieto
https://nanohub.org/members/362314
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Machine Learning with MATLAB
11 Mar 2022 | | Contributor(s):: Gaby Arellano Bello
In this session, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.
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Ogoi, Nixon W
https://nanohub.org/members/359135
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Feb 23 2022
nanoHUB Hands-On Workshop: Machine Learning with MATLAB
Abstract: Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find...
https://nanohub.org/events/details/2159