238 citations · 283 across the 8 of their papers we have counts for
18 papers · 1 filter
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…
Grain boundary solute segregation across the 5D space of crystallographic character
Lydia Harris Serafin, Ethan R. Cluff, Gus L. W. Hart +1
Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied…
Optimal Routes to Ultrafast Polarization Reversal in Ferroelectric LiNbO3
R. Tanner Hardy, Conrad Rosenbrock, Gus L. W. Hart +1
We use the frozen phonon method to calculate the anharmonic potential energy surface and to model the ultrafast ferroelectric polarization reversal in LiNbO3 driven by intense puls…
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…
Machine Learning Predictions of High-Curie-Temperature Materials
Joshua F. Belot, Valentin Taufour, Stefano Sanvito +1
Technologies that function at room temperature often require magnets with a high Curie temperature, , and can be improved with better materials. Discovering magnetic…
Effectiveness of smearing and tetrahedron methods: best practices in DFT codes
Jeremy J. Jorgensen, Gus L. W. Hart
Density functional theory (DFT) codes are commonly treated as a "black box" in high-throughput screening of materials, with users opting for the default values of the input paramet…