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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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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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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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Chemprop Demo
Tools | 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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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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Data Analysis with MATLAB
Online Presentations | 04 Mar 2022 | Contributor(s):: Gen Sasaki
Learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your coursework as well as in research.
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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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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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Polymer Genetic Algorithm
Tools | 05 Nov 2021 | Contributor(s):: Joseph D Kern
Generalized genetic algorithm designed for materials discovery.
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Autonomous Neutron Diffraction Explorer
Tools | 01 Nov 2021 | Contributor(s):: Austin McDannald
Autonomously control neutron diffraction experiments to discover order parameter.
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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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MATLAB R2021a
Tools | 09 Sep 2021 | Contributor(s):: Gen Sasaki, Lisa Kempler
MATLAB is a programming and numeric computing platform to analyze data, develop algorithms, and create models.
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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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Debugging Neural Networks
Tools | 07 Aug 2021 | Contributor(s):: Rishi P Gurnani
Debug common errors in neural networks.
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ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
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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.