140 citations · 1k across the 66 of their papers we have counts for
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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…
Efficient real space formalism for hybrid density functionals
Xin Jing, Phanish Suryanarayana
We present an efficient real space formalism for hybrid exchange-correlation functionals in generalized Kohn-Sham density functional theory (DFT). In particular, we develop an effi…
Spectral scheme for atomic structure calculations in density functional theory
Sayan Bhowmik, John E. Pask, Andrew J. Medford +1
We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation o…
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…