most citedDescribe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters

23 citations · 23 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.mtrl-sci2024★ 23 cited

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…

physics.app-ph2024

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…