| 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 | 738 | 2009 |
| 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 | 521 | 2022 |
| 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 | 430 | 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 | 365 | 2018 |
| A computer vision approach for automated analysis and classification of microstructural image data BL DeCost, EA Holm Computational materials science 110, 126-133, 2015 | 308 | 2015 |
| 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 | 303 | 2006 |
| How grain growth stops: A mechanism for grain-growth stagnation in pure materials EA Holm, SM Foiles Science 328 (5982), 1138-1141, 2010 | 293 | 2010 |
| On abnormal subgrain growth and the origin of recrystallization nuclei EA Holm, MA Miodownik, AD Rollett Acta Materialia 51 (9), 2701-2716, 2003 | 268 | 2003 |
| 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 | 263 | 2015 |
| On misorientation distribution evolution during anisotropic grain growth EA Holm, GN Hassold, MA Miodownik Acta Materialia 49 (15), 2981-2991, 2001 | 262 | 2001 |
| 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 | 249 | 1991 |
| 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 | 234 | 2017 |
| The computer simulation of microstructural evolution EA Holm, CC Battaile Jom 53, 20-23, 2001 | 229 | 2001 |
| 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 | 211 | 2019 |
| 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 | 197 | 2020 |
| 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 | 188 | 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 | 181 | 2010 |
| In defense of the black box EA Holm Science 364 (6435), 26-27, 2019 | 160 | 2019 |
| Computer vision and machine learning for autonomous characterization of AM powder feedstocks BL DeCost, H Jain, AD Rollett, EA Holm Jom 69 (3), 456-465, 2017 | 152 | 2017 |
| 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 | 151 | 2013 |
