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SCALE RH Machine-learning Based Optimization of Materials for Microelectronics
Online Presentations | 16 May 2024 | Contributor(s):: Shrienidhi Gopalakrishnan
The purpose of this research is to explore the topic of radiation damage and its effect on material degradation. The effects of radiation damage can be seen in many places, from nuclear systems to space. Current simulation techniques are both expensive and difficult to use. This project aims to...
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The Ultimate SuperComputer-on-a-Chip for Massive Big Data and Highly Iterative Algorithms
Online Presentations | 10 Apr 2024 | Contributor(s):: Veljko M. Milutinovic
ECE 606: Solid State Devices I - Guest LectureThis presentation analyses the essence of DataFlow SuperComputing, defines its advantages and sheds lighton the related programming model.DataFlow computers, compared to ControlFlow computers, offer speedups of 20 to 200 (even 2000 for some...
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Supplementary Data for "An unsupervised machine learning based approach to identify efficient spin-orbit torque materials"
Downloads | 18 Feb 2024 | Contributor(s):: Shehrin Sayed, Hannah Kleidermacher, Giulianna Hashemi-Asasi, Cheng-Hsiang Hsu, Sayeef Salahuddin
Introduction:There has been a growing interest in materials with large spin-orbit torques (SOT) for many novel applications, and in our article [1], which is currently under review, we have shown that a machine-learning-based approach using a word embedding model can predict...
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nanoHUB: AI, Data, and Simulations for Students, Researchers and Instructors
Online Presentations | 11 Jan 2024 | Contributor(s):: Alejandro Strachan, The Micro Nano Technology - Education Center
This talk will introduce nanoHUB resources for physics-based simulations, machine learning, and collaboration that can be used by students and instructors in research and education.
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Resources and Cyberinfrastructure for Laser Powder Bed Fusion – Tools to enable 3D Additive Metals Manufacturing
Online Presentations | 11 Jan 2024 | Contributor(s):: Elif Ertekin, The Micro Nano Technology - Education Center
We will describe laser powder bed fusion, how machine learning and modeling/simulation tools can help optimize the process, and opportunities to engage students in the work.
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Data-Driven Materials Innovation: where Machine Learning Meets Physics
Online Presentations | 29 Nov 2023 | Contributor(s):: Anand Chandrasekaran
Learn how Schrödinger’s tools can address common issues by using a combination of physics-based simulation data, enterprise informatics, and chemistry-aware ML.
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Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR
Online Presentations | 29 Nov 2023 | Contributor(s):: Michael Rauch
In this workshop, we will demonstrate the hands-on use of Schrödinger's MS Maestro graphical user interface within nanoHUB to perform machine learning model creation and implementation.
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Scientific Text-Based Machine Learning
Downloads | 02 Nov 2023 | Contributor(s):: Shehrin Sayed
Please note that this page is under development and information provided may change. Introduction: Machine learning is undoubtedly a useful tool and is gradually changing how we function in everyday life. The application of this powerful tool in materials and devices research may have...
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Schrödinger Materials Science AutoQSAR for Machine Learning
Tools | 11 Sep 2023
Build quantitative structure-activity relationships (QSAR) automatically for molecular systems with Schrödinger's AutoQSAR tool
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ANN-based friction factor and Nusselt number models for developing flow across square pin fins
Tools | 30 May 2023 | Contributor(s):: Saeel Shrivallabh Pai, Justin A. Weibel
ANN-based correlations which provide friction factor and Nusselt number values for developing flows across square pin fins of different pitch.
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Gaussian Process Regression for Surface Interpolation
Online Presentations | 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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No-code ML models
Tools | 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
Online Presentations | 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
Online Presentations | 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
Online Presentations | 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
Animations | 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
Animations | 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
Tools | 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
Tools | 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
Online Presentations | 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....