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MatSci 395 Lecture 1: Introduction to Machine Learning, Materials Imaging, and Segmentation
29 Sep 2021 | | Contributor(s):: Tiberiu Stan
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Hands On(line) Lab Education with Remote SEM
27 Sep 2021 | | Contributor(s):: Zackary Gray, Robert Ehrmann, NACK Network
In a year where remote communication and learning has become a necessity, delivering hands-on activities has remained a challenge. During this webinar we will present methods in which students can operate advanced scientific instrumentation without ever needing to leave their home. The...
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A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
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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Novel Two-dimensional (2D) Materials and Devices for Biomimetic Sensing and Computing
16 Aug 2021 | | Contributor(s):: Saptarshi Das, NACK Network
Many animals outsmart humans in sensory skills. In fact, animals can do much more than just see, smell, touch, taste, and hear. For example, octopuses possess polarized vision, bats use ultrasound to echolocate, hyper touch sensitive spiders can trace the origin of micro-vibrations and sharks can...
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Shape-Changing Micromachines
05 Aug 2021 | | Contributor(s):: Daniel Lopez, NACK Network
This presentation will introduce the fundamentals and limitations of current micro-machines and discuss the prospect of creating shape morphing structures by using origami and Kirigami techniques combined with nanoscale materials.
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ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
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
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
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.
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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
25 Jun 2021 | | Contributor(s):: Ryan Jacobs
This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction.
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A Hands-on Introduction to Physics-Informed Neural Networks
16 Jun 2021 | | Contributor(s):: Ilias Bilionis, Atharva Hans
Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural...
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Bayesian optimization tutorial using Jupyter notebook
11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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SimTools: Delivering Simulations in the Era of Abundant Data
04 Jun 2021 | | Contributor(s):: Alejandro Strachan
This presentation introduces SimTool, a library that allows developers to create, publish, and share reproducible workflows with well-defined and verified inputs and outputs.
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Model Rockets and Composite Materials: Design, Build, Launch
27 May 2021 | | Contributor(s):: Amber Genau
Students will gain experience with polymer matrix fiber composites, composite production, and the tradeoffs inherent in the engineering design process by designing, building and launching their own model rocket. Composite materials are created via hand layup and vacuum assisted resin...
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May 26 2021
A Hands-on Introduction to Physics-Informed Neural Networks
Presenter:Ilias Bilionis, Purdue UniversityAbstract:Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential...
https://nanohub.org/events/details/1980
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Materials for Hydrogen-Based Energy Conversion
25 May 2021 | | Contributor(s):: Nicole Shuman, Susan P Gentry
Simulate the effects different materials have on hydrogen-based energy conversion.
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A Hands-on Introduction to Physics-Informed Neural Networks
21 May 2021 | | Contributor(s):: Atharva Hans, Ilias Bilionis
A Hands-on Introduction to Physics-Informed Neural Networks
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May 19 2021
The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Presenter:Ryan Jacobs, University of Wisconsin-MadisonAbstract:This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package...
https://nanohub.org/events/details/1979
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Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial
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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Characterizing Electrolytic Materials
31 Mar 2021 | | Contributor(s):: Steven Kandel, NNCI Nano
The lab is designed to help students understand that the resistance of an object depends on length, cross-sectional area, and the type of material. Students measure the current through objects to see that different materials resist current in different amounts. Students will find that,...
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Thermo-Calc Educational Package
23 Mar 2021 | | Contributor(s):: Paul Mason, Alejandro Strachan
Thermo-Calc Educational Package