Elizabeth A. Holm
Cited by
Cited by
Survey of computed grain boundary properties in face-centered cubic metals: I. Grain boundary energy
DL Olmsted, SM Foiles, EA Holm
Acta Materialia 57 (13), 3694-3703, 2009
Survey of computed grain boundary properties in face-centered cubic metals—II: Grain boundary mobility
DL Olmsted, EA Holm, SM Foiles
Acta materialia 57 (13), 3704-3713, 2009
Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering
DM Dimiduk, EA Holm, SR Niezgoda
Integrating Materials and Manufacturing Innovation 7, 157-172, 2018
Computing the mobility of grain boundaries
KGF Janssens, D Olmsted, EA Holm, SM Foiles, SJ Plimpton, PM Derlet
Nature materials 5 (2), 124-127, 2006
Recent advances and applications of deep learning methods in materials science
K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ...
npj Computational Materials 8 (1), 59, 2022
A computer vision approach for automated analysis and classification of microstructural image data
BL DeCost, EA Holm
Computational materials science 110, 126-133, 2015
How grain growth stops: A mechanism for grain-growth stagnation in pure materials
EA Holm, SM Foiles
Science 328 (5982), 1138-1141, 2010
On misorientation distribution evolution during anisotropic grain growth
EA Holm, GN Hassold, MA Miodownik
Acta Materialia 49 (15), 2981-2991, 2001
On abnormal subgrain growth and the origin of recrystallization nuclei
EA Holm, MA Miodownik, AD Rollett
Acta Materialia 51 (9), 2701-2716, 2003
Effects of lattice anisotropy and temperature on domain growth in the two-dimensional Potts model
EA Holm, JA Glazier, DJ Srolovitz, GS Grest
Physical Review A 43 (6), 2662, 1991
Grain boundary energies in body-centered cubic metals
S Ratanaphan, DL Olmsted, VV Bulatov, EA Holm, AD Rollett, GS Rohrer
Acta Materialia 88, 346-354, 2015
The computer simulation of microstructural evolution
EA Holm, CC Battaile
Jom 53, 20-23, 2001
Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures
BL DeCost, T Francis, EA Holm
Acta Materialia 133, 30-40, 2017
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
BL DeCost, B Lei, T Francis, EA Holm
Microscopy and Microanalysis 25 (1), 21-29, 2019
Boundary mobility and energy anisotropy effects on microstructural evolution during grain growth
M Upmanyu, GN Hassold, A Kazaryan, EA Holm, Y Wang, B Patton, ...
Interface Science 10, 201-216, 2002
Comparing grain boundary energies in face-centered cubic metals: Al, Au, Cu and Ni
EA Holm, DL Olmsted, SM Foiles
Scripta Materialia 63 (9), 905-908, 2010
Overview: Computer vision and machine learning for microstructural characterization and analysis
EA Holm, R Cohn, N Gao, AR Kitahara, TP Matson, B Lei, SR Yarasi
Metallurgical and Materials Transactions A 51, 5985-5999, 2020
In defense of the black box
EA Holm
Science 364 (6435), 26-27, 2019
Crossing the mesoscale no-man’s land via parallel kinetic Monte Carlo
S Plimpton, C Battaile, M Chandross, L Holm, A Thompson, V Tikare, ...
Sandia Report SAND2009-6226 1, 2009
Phenomenology of shear-coupled grain boundary motion in symmetric tilt and general grain boundaries
ER Homer, SM Foiles, EA Holm, DL Olmsted
Acta Materialia 61 (4), 1048-1060, 2013
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