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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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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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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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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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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....
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Machine Learning with MATLAB
Online Presentations | 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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Integrating Machine Learning with a Genetic Algorithm for Materials Exploration
Online Presentations | 07 Dec 2021 | Contributor(s):: Joseph D Kern
In this talk, we will explore how this algorithm can be used for materials discovery.
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Designing Machine Learning Surrogates for Molecular Dynamics Simulations
Online Presentations | 25 Nov 2021 | Contributor(s):: JCS Kadupitiya
Molecular dynamics (MD) simulations accelerated by high-performance computing (HPC) methods are powerful tools for investigating and extracting the microscopic mechanisms characterizing the properties of soft materials such as self-assembled nanoparticles, virus capsids, confined electrolytes,...
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Autonomous Neutron Diffraction Experiments with ANDiE
Online Presentations | 14 Nov 2021 | Contributor(s):: Austin McDannald
This tutorial will cover the working principles of ANDiE, how physics was encoded into the design, and demonstrate how ANDiE can be used to autonomously control neutron diffraction experiments.
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MatSci 395 Lecture 5: MatCNN In-Class Tutorial
Online Presentations | 01 Nov 2021 | Contributor(s):: Tiberiu Stan
Access MatCNN by clicking here
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MatSci 395 Lecture 6: How Do Convolutional Neural Networks Work?
Online Presentations | 01 Nov 2021 | Contributor(s):: Tiberiu Stan
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MatSci 395 Lecture 4: Neural Network Training
Online Presentations | 07 Oct 2021 | Contributor(s):: Tiberiu Stan
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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