Tags: materials science

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Description

Materials science is the understanding and application of properties of matter. Materials science studies the connections between the structure of a material, its properties, methods of processing and performance for given applications.

Please see the nanoHUB Group Materials Science for highlighted materials science related items.

For educators please see the nanoHUB group MSE Instructional Exchange

For the latest tools that combine materials science with machine learning and data science see the nanoHUB group Data Science and Machine Learning

Tools (1-20 of 118)

  1. Schrödinger Materials Science AutoQSAR for Machine Learning

    Tools | 11 Sep 2023

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

  2. Mechanical response of materials using Jupyter

    Tools | 31 Jan 2023 | Contributor(s):: Alejandro Strachan

    This tool provides mathematical tools using python in Jupyter to explore and calculate mechanical properties of materials

  3. Refractory Oxidation Database

    Tools | 19 Apr 2022 | Contributor(s):: Saswat Mishra, Sharmila Karumuri, Vincent Joseph Mika, Collin Conn Scott, Michael S Titus, Ilias Bilionis, Alejandro Strachan

    Creates a database of refractory alloys oxidation

  4. Autonomous Neutron Diffraction Explorer

    Tools | 01 Nov 2021 | Contributor(s):: Austin McDannald

    Autonomously control neutron diffraction experiments to discover order parameter.

  5. ML-aided High-throughput screening for Novel Oxide Perovskite Discovery

    Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra

    ML-based tool to discover novel oxide perovskites with wide band gaps

  6. Bayesian optimization tutorial using Jupyter notebook

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

    Active learning via Bayesian optimization for materials discovery

  7. Materials for Hydrogen-Based Energy Conversion

    Tools | 25 May 2021 | Contributor(s):: Nicole Shuman, Susan P Gentry

    Simulate the effects different materials have on hydrogen-based energy conversion.

  8. A Hands-on Introduction to Physics-Informed Neural Networks

    Tools | 21 May 2021 | Contributor(s):: Atharva Hans, Ilias Bilionis

    A Hands-on Introduction to Physics-Informed Neural Networks

  9. Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial

    Tools | 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.

  10. Thermo-Calc Educational Package

    Tools | 23 Mar 2021 | Contributor(s):: Paul Mason, Alejandro Strachan

    Thermo-Calc Educational Package

  11. MIT Atomic-Scale Modeling Toolkit

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

    Tools for Atomic-Scale Modeling

  12. Materials Graph Network

    Tools | 21 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo

    Materials Graph Networks for molecule and crystal structure-property relationship modeling

  13. Machine Learning Force Field for Materials

    Tools | 27 Oct 2020 | Contributor(s):: Chi Chen, Yunxing Zuo

    Machine learning force field for materials

  14. SEM Image Segmentation Workshop

    Tools | 10 Dec 2020 | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin

    This tool introduces users to machine learning used to segment microscopy images

  15. PolymerXtal - Polymer Crystal Structure Generator and Analysis Software

    Tools | 13 Oct 2020 | Contributor(s):: Tongtong Shen, Jessica Nash, Alejandro Strachan

    PolymerXtal is a software designed to build and analyze molecular-level polymer crystal structures.

  16. Machine Learning Defect Behavior in Semiconductors

    Tools | 09 Nov 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)

    Develop machine learning models to predict defect formation energies in chalcogenides

  17. Linear Regression Young's modulus

    Tools | 16 Sep 2020 | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan

    Use linear regression to extract Young's modulus and yield stress from stress-strain data

  18. Data Analysis of Normal Data Sets in Engineering

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

    Statistical and data analysis concepts in engineering

  19. Matlab Data Analysis Using Jupyter Notebooks

    Tools | 21 Jul 2020 | Contributor(s):: Jon Nykiel, Anna Leichty, Zachary D McClure, Alejandro Strachan, Aileen Ryan, Adrian Nat Gentry, Amanda Johnston, Tamara Jo Moore, Allen Garner, Peter Bermel

    Use Jupyter Notebooks with a Matlab kernel running in the background for data analysis and intro to engineering homework problems

  20. Machine Learning Lab Module

    Tools | 27 Feb 2020 | Contributor(s):: BENJAMIN AFFLERBACH, Rundong Jiang, Josh Tappan, DANE MORGAN

    A lab activity for introduction to machine learning in materials science