Tags: material properties

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  1. 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.

  2. Polymer Genetic Algorithm

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

    Generalized genetic algorithm designed for materials discovery.

  3. 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...

  4. 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...

  5. 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.

  6. 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.

  7. Parsimonious Neural Networks Learn Interpretable Physical Laws

    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...

  8. Carbon Nanotube Fracture

    27 May 2021 | | Contributor(s):: Christine M Aikens, George C. Schatz, Marcelo Carignano

    Due to their mechanical properties, carbon nanotubes (CNTs) hold promise as nanoreinforcements in a variety of composites. As a result, numerous theoretical and experimental studies have been performed in order to understand the behavior of CNTs under axial tension. Whereas quantum mechanical...

  9. Convenient and efficient development of Machine Learning Interatomic Potentials

    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.

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

    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.

  11. Symposium on Nanomaterials for Energy: Atomic Force Microscopy for Energy Applications - A Review

    05 Feb 2021 | | Contributor(s):: Arvind Raman

    Atomic Force Microscopy is unique in its ability to measure sub -nanonewton forces arising from a variety physical phenomena between a sharp tip and a sample. In this talk we review the most recent applications of atomic force microscopy to explore and characterize quantitatively the properties...

  12. Shape-changing Nanoparticles for Nanomedicine Applications

    04 Feb 2021 | | Contributor(s):: Vikram Jadhao

    I will describe the nanoHUB tool “Nanoparticle Shape Lab” that enables simulations of the shape deformation of charged nanoparticles for a broad variety of nanoparticle material properties including nanoparticle surface charge, pattern, elasticity and solution conditions such...

  13. Machine Learning Force Field for Materials

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

    Machine learning force field for materials

  14. Machine Learning Framework for Impurity Level Prediction in Semiconductors

    15 Dec 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi

    In this work, we perform screening of functional atomic impurities in Cd-chalcogenide semiconductors using high-throughput computations and machine learning.

  15. Machine Learning Defect Behavior in Semiconductors

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

    Develop machine learning models to predict defect formation energies in chalcogenides

  16. Linear Regression Young's modulus

    24 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

  17. The Eötvös Paradox: The Enduring Significance of Eötvös' Most Famous Experiment

    23 Aug 2020 | | Contributor(s):: Ephraim Fischbach

    Here we summarize the key elements of this "Eötvös paradox," and suggest some possible paths to a resolution. Along the way we also discuss the close relationship between Eötvös and Einstein, and consider how their respective contributions may have been influenced...

  18. Aerogel (Aerogel)

    02 Jul 2020 | | Contributor(s):: Nano-Link Center for Nanotechnology Education, Carol E. Bouvier, James J Marti, Rodfal Alberto Rodriguez (editor), María Teresa Rivera (editor)

    Los aerogeles son las sustancias semisólidas conocidas más livianas que existen. Los aerogeles de sílice o dióxido de silicio (SiO2) tienen la misma estructura química que la arena de sílice o el vidrio de las ventanas, pero tienen diferencias...

  19. Mark Knackstedt

    Mark Knackstedtis Professor at the Department of Applied Mathematics at the Australian National University (ANU). He received his BSc degree at Columbia University and PhD in Chemical Engineering...


  20. Mystery Molecules: Identifying Materials with Nanoscale Characterization Tools

    18 Mar 2020 | | Contributor(s):: Maude Cuchiara, NNCI Nano

     In this lesson plan, students will be given several similar looking materials and asked to identify them by observing them at the macro and micro-scale. They will then be exposed to different analytical tools and describe how they can be used to explore materials at the nanoscale. ...