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Materials Science

D-Index
47
Citations
9270
World Ranking
11067
National Ranking
2604

Overview

Elizabeth A. Holm is affiliated with the University of Michigan-Ann Arbor in the United States. Their research spans multiple fields within materials science and engineering, with a focus on the integration of computational methods and material characterization.

The main fields of study for Elizabeth A. Holm include:

  • Materials Science
  • Engineering

Their subfields of study show a strong interdisciplinary approach, involving:

  • Materials Chemistry
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

Central topics in their work cover:

  • Machine Learning in Materials Science
  • Microstructure and mechanical properties
  • Mineral Processing and Grinding
  • Industrial Vision Systems and Defect Detection
  • Microstructure and Mechanical Properties of Steels
  • Electron and X-Ray Spectroscopy Techniques
  • Domain Adaptation and Few-Shot Learning

Their publication record includes numerous papers in several prominent scientific venues where they most frequently publish:

  • Acta Materialia
  • SSRN Electronic Journal
  • JOM
  • arXiv (Cornell University)
  • Annual Review of Materials Research

Recent papers authored or co-authored by Elizabeth A. Holm cover a range of computational and experimental materials science topics. Selected papers include:

  • Recent advances and applications of deep learning methods in materials science, 2022, npj Computational Materials
  • A deep learning approach for complex microstructure inference, 2021, Nature Communications
  • Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data, 2021, Integrating materials and manufacturing innovation
  • Machine-Learning Microstructure for Inverse Material Design, 2021, Advanced Science
  • A transfer learning approach for improved classification of carbon nanomaterials from TEM images, 2020, Nanoscale Advances

Collaborations form an important aspect of their research, with frequent co-authors including:

  • Ian Chesser
  • Ryan Cohn
  • Bo Lei
  • Anthony D. Rollett
  • Martin Müller

Best Publications

  • Recent Advances and Applications of Deep Learning Methods in Materials Science

    Kamal Choudhary;Brian DeCost;Chi Chen;Anubhav Jain

  • Survey of computed grain boundary properties in face-centered cubic metals: I. Grain boundary energy

    David L. Olmsted;Stephen M. Foiles;Elizabeth A. Holm

  • Survey of computed grain boundary properties in face-centered cubic metals—II: Grain boundary mobility

    Unknown

  • Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

    Dennis M. Dimiduk;Elizabeth A. Holm;Stephen R. Niezgoda

  • A computer vision approach for automated analysis and classification of microstructural image data

    Brian L. DeCost;Elizabeth A. Holm

  • Computing the mobility of grain boundaries.

    Koenraad G. F. Janssens;Koenraad G. F. Janssens;David Olmsted;Elizabeth A. Holm;Stephen M. Foiles

  • How Grain Growth Stops: A Mechanism for Grain-Growth Stagnation in Pure Materials

    Elizabeth A. Holm;Stephen M. Foiles

  • Grain boundary energies in body-centered cubic metals

    Sutatch Ratanaphan;Sutatch Ratanaphan;David L. Olmsted;Vasily V. Bulatov;Elizabeth A. Holm

  • On misorientation distribution evolution during anisotropic grain growth

    Elizabeth A. Holm;Gregory N. Hassold;Mark A. Miodownik

  • On abnormal subgrain growth and the origin of recrystallization nuclei

    E A Holm;Mark Miodownik;A D Rollett

  • Effects of lattice anisotropy and temperature on domain growth in the two-dimensional Potts model.

    Elizabeth A. Holm;James A. Glazier;David J. Srolovitz;Gary S. Grest

  • Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures

    Brian L. DeCost;Toby Francis;Elizabeth A. Holm

  • Boundary Mobility and Energy Anisotropy Effects on Microstructural Evolution During Grain Growth

    Moneesh Upmanyu;Gregory N. Hassold;Andrei Kazaryan;Elizabeth A. Holm

  • Comparing grain boundary energies in face-centered cubic metals: Al, Au, Cu and Ni

    Elizabeth A. Holm;David L. Olmsted;Stephen M. Foiles

  • High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel.

    Brian L DeCost;Bo Lei;Toby Francis;Elizabeth A Holm

  • Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis

    Elizabeth A. Holm;Ryan Cohn;Nan Gao;Andrew R. Kitahara

  • Phenomenology of shear-coupled grain boundary motion in symmetric tilt and general grain boundaries

    Eric R. Homer;Eric R. Homer;Stephen M. Foiles;Elizabeth A. Holm;Elizabeth A. Holm;David L. Olmsted

  • Highly parallel computer simulations of particle pinning: zener vindicated

    Mark A. Miodownik;Elizabeth A. Holm;Gregory N. Hassold

  • Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Brian L. DeCost;Harshvardhan Jain;Anthony D. Rollett;Elizabeth A. Holm

  • Comparing calculated and measured grain boundary energies in nickel

    Gregory S. Rohrer;Elizabeth A. Holm;Anthony D. Rollett;Stephen M. Foiles

  • Applied machine learning to predict stress hotspots I: Face centered cubic materials

    Ankita Mangal;Elizabeth A. Holm

  • On boundary misorientation distribution functions and how to incorporate them into three-dimensional models of microstructural evolution

    M Miodownik;A.W Godfrey;E.A Holm;D.A Hughes

  • Comparison of phase-field and Potts models for coarsening processes

    V. Tikare;E. A. Holm;D. Fan;Long-qing Chen

Frequent Co-Authors

Stephen M. Foiles
Stephen M. Foiles Sandia National Laboratories
Anthony D. Rollett
Anthony D. Rollett Carnegie Mellon University
David J. Srolovitz
David J. Srolovitz University of Hong Kong
Peter J. Bentley
Peter J. Bentley University College London
Christopher R. Weinberger
Christopher R. Weinberger Colorado State University
Brad L. Boyce
Brad L. Boyce Sandia National Laboratories
Gregory S. Rohrer
Gregory S. Rohrer Carnegie Mellon University
Peter Gumbsch
Peter Gumbsch Karlsruhe Institute of Technology
Steven J. Plimpton
Steven J. Plimpton Sandia National Laboratories
Paul A. Salvador
Paul A. Salvador Carnegie Mellon University

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