Tags: material properties

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  1. Aug 21 2023

    ICANM2023: 10th International Conference & Exhibition on Advanced & Nano Materials

    The ICANM  conference and exhibition  is designed to promote information exchange among scientists, technologists, engineers, entrepreneurs and exhibitors involved in materials...

    https://nanohub.org/events/details/2239

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

    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.

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

    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.

  4. A Guide to the MIT Atomic Scale Modeling Toolkit for nanoHUB.org

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

    This document is a guide to the Quantum ESPRESSO application within the >MIT Atomic Scale Modeling Toolkit The guide was designed to be presented as part II of the nanoHUB seminar “A condensed matter physics class and a Course-based Undergraduate Research Experience (CURE) with the MIT...

  5. Chemical and Physical Properties of Endohedrally Doped Nanodiamonds

    09 Nov 2022 | | Contributor(s):: Tomekia Simeon

    The semiempirical electronic structure Parametric Method 3 (PM3) at the nanoHUB.org website is introduced to the student in this assignment. In particular, this semiempirical method is applied to study dopant semiconductor materials intercalated in two types of nanodiamond (ND) complexes:...

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

    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...

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

    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.

  8. Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties

    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.

  9. Microstructure Modeling with OOF2 and OOF3D

    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...

  10. Why You Should Care About Crystals

    21 Aug 2022 | | Contributor(s):: Aerielle Rodriguez, Rice University

    Why are Crystals important for material engineering? This project tested different crystals with varying band gaps in order to demonstrate the relationship between observable optical properties and physical properties of crystals.

  11. Properties of Nanomaterials

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

  12. Integrating Machine Learning with a Genetic Algorithm for Materials Exploration

    07 Dec 2021 | | Contributor(s):: Joseph D Kern

    In this talk, we will explore how this algorithm can be used for materials discovery.

  13. Designing Machine Learning Surrogates for Molecular Dynamics Simulations

    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,...

  14. Polymer Genetic Algorithm

    05 Nov 2021 | | Contributor(s):: Joseph D Kern

    Generalized genetic algorithm designed for materials discovery.

  15. Machine Learning in Materials Science: Image Analysis Using Convolutional Neural Networks in MatCNN

    03 Nov 2021 | | Contributor(s):: Tiberiu Stan, Jim James, Nathan Pruyne, Marcus Schwarting, Jiwon Yeom, Peter Voorhees, Ben J Blaiszik, Ian Foster, Jonathan D Emery

      This course introduces fundamental concepts of artificial intelligence within the context of materials science and image segmentation. The two-week module was taught as part of a Computational Methods in Materials Science course at Northwestern University. The module is aimed at...

  16. Andrés Felipe Sierra

    BSc Chemical Engineering & Data Scientist. Materials Science and Nanotech enthusiasm.

    https://nanohub.org/members/344047

  17. Cadmium Selenide Synthesis, Characterization and Modeling

    22 Oct 2021 | | Contributor(s):: Shelby Hatch, Evan R. Trivedi, Baudilio Tejerina, George C. Schatz

    This is a combined experiment/computational lab in which cadmium selenide quantum dot nanoparticles are synthesized, their spectra are studied, and the results are modeling using the CNDO/INDO semiempirical electronic structure code. Synthesis and Size Dependent Properties of CdSe Quantum...

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

    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...

  19. 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.

  20. 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.