Tags: materials science

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  1. Hands On(line) Lab Education with Remote SEM

    27 Sep 2021 |

    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...

  2. 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...

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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...

  8. Bayesian optimization tutorial using Jupyter notebook

    11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal

    Active learning via Bayesian optimization for materials discovery

  9. 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.

  10. 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...

  11. 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

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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,...

  17. Convenient and efficient development of Machine Learning Interatomic Potentials

    09 Mar 2021 | | Contributor(s):: Yunxing Zuo

    This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models.

  18. Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks

    09 Mar 2021 | | Contributor(s):: Chi Chen

    This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional operations are carried out on the materials graphs.

  19. MIT Atomic-Scale Modeling Toolkit

    15 Jan 2008 | | Contributor(s):: daniel richards, Elif Ertekin, Jeffrey C Grossman, David Strubbe, Justin Riley, Enrique Guerrero

    Tools for Atomic-Scale Modeling

  20. Symposium on Nanomaterials for Energy: Atomic Force Microscopy for Energy Applications - A Review

    17 May 2012 | | Contributor(s):: Arvind Raman

    Atomic Force Microscopy is unique in its ability to measure sub -nanonewton forces arising from a variety physical phenomena between a sharp tip and a sample. In this talk we review the most recent applications of atomic force microscopy to explore and characterize quantitatively the properties...