Online Presentations (1-20 of 126)

1. SCALE Student Poster: Thermal Characterization of Contemporary Electrical Insulation Materials

   Online Presentations | 15 Apr 2024 | Contributor(s):: Shanmukhi Sripada

    

2. SCALE Student Poster: Phase and Property Control of Vanadium Dioxide Thin Films

   Online Presentations | 15 Apr 2024 | Contributor(s):: Claire Mihalko

3. Exciton Ultrafast Dynamics in Nanomaterials

   Online Presentations | 07 Dec 2023 | Contributor(s):: Aziz Boulesbaa, The Micro Nano Technology - Education Center

   In this talk, I will present our experimental capabilities to study nanomaterials.  In particular, femtosecond laser spectroscopy and microscopy will be discussed....

4. An Introduction to Finite Element Analysis of Material Microstructure Properties in nanoHUB

   Online Presentations | 19 Oct 2023 | Contributor(s):: Yang Dan

   In this webinar, Yang will give a brief introduction to the fundamentals of FEA and OOF2, and demonstrate OOF2 simulations of stress distribution in example materials, with and without temperature effect.

5. ABACUS Tool Suite and MOSFETs (Fall 2023)

   Online Presentations | 19 Oct 2023 | Contributor(s):: Gerhard Klimeck

   In the seventh session, Dr. Klimeck will give a brief overview of ABACUS and demonstrate the MOSFET Lab. Students can experiment with the classical scaling of a traditional 2D MOSFET.

6. Teaching and Learning with the MIT Atomic Scale Modeling Toolkit's Classical and Quantum Atomic Modeling Applications

   Online Presentations | 23 Dec 2022 | Contributor(s):: Enrique Guerrero

    We will perform molecular dynamics computations using LAMMPS, simple Monte Carlo simulations including the Ising model, and run quantum chemistry and density functional theory computations.

7. Interactive Modeling of Materials with Density Functional Theory Using the Quantum ESPRESSO Interface within the MIT Atomic Scale Modeling Toolkit

   Online Presentations | 22 Nov 2022 | Contributor(s):: Enrique Guerrero

   We will explore the Quantum ESPRESSO interface within the MIT Atomic-Scale Modeling Toolkit with interactive examples. We will review the basics of density functional theory and then focus on the tool’s capabilities.

8. 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...

9. Introduction to Computational Chemistry Using the NUITNS Simulation Toolkit in nanoHUB

   Online Presentations | 06 Oct 2022 | Contributor(s):: Tomekia Simeon

   In this seminar, Dr. Tomekia Simeon will describe how she has successfully used computational chemistry assignments in her undergraduate chemistry courses at Dillard University using nanoHUB’s free online simulation resources.

10. 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.

11. Microstructure Modeling with OOF2 and OOF3D

    Online Presentations | 26 Aug 2022 | Contributor(s):: Andrew Reid, Stephen Langer

    The OOF object-oriented finite element software, developed at the National Institute of Standards and Technology, provides an interactive FEM tool which packages sophisticated mathematical capabilities with a user-interface that speaks the language of materials science...

12. Properties of Nanomaterials

    Online Presentations | 30 Jul 2022 | Contributor(s):: Peter Kazarinoff, Mariel Kolker, NACK Network

13. 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.

14. 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,...

15. IWCN 2021: Recursive Open Boundary and Interfaces Method for Material Property Predictions

    Online Presentations | 14 Jul 2021 | Contributor(s):: James Charles, Sabre Kais, Tillmann Christoph Kubis

    In this presentation, we show that assuming periodicity elevates a small perturbation of a periodic cell into a strong impact on the material property prediction. Periodic boundary conditions can be applied on truly periodic systems only. More general systems should apply an open boundary...

16. 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.

17. The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction

    Online Presentations | 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.

18. Parsimonious Neural Networks Learn Interpretable Physical Laws

    Online Presentations | 21 Jun 2021 | Contributor(s):: Saaketh Desai

    Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary...

19. Convenient and efficient development of Machine Learning Interatomic Potentials

    Online Presentations | 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.

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

    Online Presentations | 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.