Tags: computational materials science


Computational materials science is the application of computational methods alone or in conjunction with experimental techniques to discover new materials and investigate existing materials such as: metals, ceramics, composites, semiconductors, nanostructures, 2D materials, metamaterials, polymers, liquid crystals, surfactants, emulsions, polymer nanocomposites, nanocrystal superlattices and nanoparticles.

All Categories (1-20 of 185)

  1. Schrödinger Materials Science AutoQSAR for Machine Learning

    11 Sep 2023 |

    Build quantitative structure-activity relationships (QSAR) automatically for molecular systems with Schrödinger's AutoQSAR tool

  2. Exploring the Nano World: Building Nanoscale Structures with Polymer Modeler

    14 Jul 2023 | | Contributor(s):: Tongtong Shen

    In this talk, I will showcase how atomic-level simulations can lead to a more fundamental understanding of PAN crystal structures and guide you through an interactive Polymer Modeler powered by nanoHUB.

  3. Raphael N Ogbodo


  4. Jun 09 2023

    Exploring the Nano World: Building Nanoscale Structures with Polymer Modeler

    Exploring the Nano World: Building Nanoscale Structures with Polymer ModelerPresenter:Dr. Tongtong (Tanya) Shen, AppleDate and Time:June 9, 2023; 2:00 - 3:00 PM EDTRegister hereAbstract:The...


  5. A Condensed Matter Physics class and a Course-Based Undergraduate Research Experience (CURE) with the MIT Atomic-Scale Modeling Toolkit

    07 Nov 2022 | | Contributor(s):: David Strubbe

    In this presentation, Dr. Strubbe will discuss how he has been using the MIT Atomic-Scale Modeling Toolkit as a part of his undergraduate and graduate class on condensed matter physics. In discussion sections, simulations are performed to illustrate concepts like covalent bonding,...

  6. Density Functional Theory: Introduction and Applications

    07 Nov 2022 | | Contributor(s):: André Schleife

    In this webinar, Dr. Schleife will briefly outline the fundamentals of DFT, and demonstrate how to use Quantum Espresso in nanoHUB to compute electronic structure, electronic densities of state, total energies, and bulk modulus for example materials.

  7. 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...

  8. Back to School Webinar Series on Teaching with nanoHUB

    06 Oct 2022 |

    These webinars are opportunities to learn from faculty who use nanoHUB in their classes and research.    The webinars will generally focus on using nanoHUB simulation apps and assignments or aspects of teaching or doing research using nanoHUB resources.

  9. Introduction to DFT simulations in nanoHUB

    06 Oct 2022 | | Contributor(s):: André Schleife

    In this webinar, I will briefly outline the fundamentals of this technique, and demonstrate applications to compute total energies, bulk modulus, and electronic structure/densities of states using Nanohub.

  10. 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.

  11. Saeed Norouzi


  12. Reitesh KV Raman


  13. Machine Learning in Physics

    04 Nov 2021 | | Contributor(s):: Nicolas Onofrio

    Lectures and tutorials to learn how to write machine learning programs with Python

  14. Andrés Felipe Sierra

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


  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. 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

  17. Krishna Sai Kaligotla


  18. Machine Learning Defect Behavior in Semiconductors

    09 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi

    Develop machine learning models to predict defect formation energies in chalcogenides

  19. Jeffrey W Bullard


  20. Data Analysis of Normal Data Sets in Engineering

    04 Jun 2020 | | Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki

    Statistical and data analysis concepts in engineering