Aug 21 2023
ICANM2023: 10th International Conference & Exhibition on Advanced & Nano Materials
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.
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.
A Guide to the MIT Atomic Scale Modeling Toolkit for nanoHUB.org
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...
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:...
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...
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.
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.
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...
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.
Properties of Nanomaterials
30 Jul 2022 | | Contributor(s):: Mariel Kolker, Peter Kazarinoff, NACK Network
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.
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,...
Polymer Genetic Algorithm
05 Nov 2021 | | Contributor(s):: Joseph D Kern
Generalized genetic algorithm designed for materials discovery.
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...
Andrés Felipe Sierra
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...
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...
An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
25 Jun 2021 | | Contributor(s):: Benjamin Afflerbach
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.