112 citations · 269 across the 7 of their papers we have counts for
7 papers
Facet and energy predictions in grain boundaries: lattice matching and molecular dynamics
Bruno Dobrovolski, C. Braxton Owens, Gus L. W. Hart +2
Many material properties can be traced back to properties of their grain boundaries. Grain boundary energy (GBE), as a result, is a key quantity of interest in the analysis and mod…
Relative Grain Boundary Energies from Triple Junction Geometry: Limitations to Assuming the Herring Condition in Nanocrystalline Thin Films
Matthew J. Patrick, Gregory S. Rohrer, Ooraphan Chirayutthanasak +5
Grain boundary character distributions (GBCD) are routinely measured from bulk microcrystalline samples by electron backscatter diffraction (EBSD) and serial sectioning can be used…
Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character
Eric R. Homer, Gus L. W. Hart, C. Braxton Owens +3
The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic degrees of freedom that define the character of a grain boundary. While numerous datas…
Five Degree-of-Freedom Property Interpolation of Arbitrary Grain Boundaries via Voronoi Fundamental Zone Octonion Framework
Sterling G. Baird, Eric R. Homer, David T. Fullwood +1
We introduce the Voronoi fundamental zone octonion interpolation framework for grain boundary (GB) structure-property models and surrogates. The VFZO framework offers an advantage…
Atomistic survey of grain boundary dislocation interactions in FCC Nickel
Devin W Adams, David T Fullwood, Robert H Wagoner +1
It is well known that grain boundaries (GBs) have a strong influence on mechanical properties of polycrystalline materials. Not as well-known is how different GBs interact with dis…
Structural Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility
Conrad W. Rosenbrock, Jonathan L. Priedeman, Gus L. W. Hart +1
Recent advances in the numerical representation of materials opened the way for successful machine learning of grain boundary (GB) energies and the classification of GB mobility an…
Discovering the Building Blocks of Atomic Systems using Machine Learning
Conrad W. Rosenbrock, Eric R. Homer, Gábor Csányi +1
Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is ne…