20 citations · 33 across the 14 of their papers we have counts for
5 papers · 1 filter
SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations
Tian Tian, Lucas R Timmerman, Shashikant Kumar +3
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations…
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…
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
Lucas R. Timmerman, Shashikant Kumar, Phanish Suryanarayana +1
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the…
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…