1 citations · 1 across the 4 of their papers we have counts for
4 papers
Cyclic and helical symmetry-informed machine learned force fields: Application to lattice vibrations in carbon nanotubes
Abhiraj Sharma, Shashikant Kumar, Phanish Suryanarayana
We present a formalism for developing cyclic and helical symmetry-informed machine learned force fields (MLFFs). In particular, employing the smooth overlap of atomic positions des…
Shock Hugoniot calculations using on-the-fly machine learned force fields with ab initio accuracy
Shashikant Kumar, John E. Pask, Phanish Suryanarayana
We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model b…
On-the-fly machine learned force fields for the study of warm dense matter: application to diffusion and viscosity of CH
Shashikant Kumar, Xin Jing, John E. Pask +1
We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme bas…
Kohn-Sham accuracy from orbital-free density functional theory via -machine learning
Shashikant Kumar, Xin Jing, John E. Pask +2
We present a -machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned…