23 citations · 26 across the 3 of their papers we have counts for
3 papers
Describe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters
C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson +5
Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to r…
Learning from metastable grain boundaries
Avanish Mishra, Sumit A. Suresh, Saryu J. Fensin +2
Grain boundaries (GBs) govern critical properties of polycrystals. Although significant advancements have been made in characterizing minimum energy GBs, real GBs are seldom found…
Strain Functionals: A Complete and Symmetry-adapted Set of Descriptors to Characterize Atomistic Configurations
Edward M. Kober, Jacob P. Tavenner, Colin M. Adams +1
Extracting relevant information from atomistic simulations relies on a complete and accurate characterization of atomistic configurations. We present a framework for characterizing…