Shashikant Kumar

Georgia Institute of Technology

H-index: 4

North America-United States

About Shashikant Kumar

Shashikant Kumar, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Georgia Institute of Technology, specializes in the field of Density functional theory, Real space Density functional theory, Nanomaterials, Scientific ML.

His recent articles reflect a diverse array of research interests and contributions to the field:

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations

On-the-fly machine learned force fields for the study of warm dense matter: application to diffusion and viscosity of CH

Review of the second charged-particle transport coefficient code comparison workshop

On the bending of rectangular atomic monolayers along different directions: an ab initio study

Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning

Version 2.0. 0--SPARC: Simulation Package for Ab-initio Real-space Calculations

Version 2.0. 0-M-SPARC: Matlab-Simulation Package for Ab-initio Real-space Calculations

Accurate parameterization of the kinetic energy functional

Shashikant Kumar Information

University	Georgia Institute of Technology
Position	Ph.D. in computational mechanics/chemistry at .
Citations(all)	92
Citations(since 2020)	92
Cited By	21
hIndex(all)	4
hIndex(since 2020)	4
i10Index(all)	3
i10Index(since 2020)	3
Email	Access Email
University Profile Page	Georgia Institute of Technology
Google Scholar	View Google Scholar Profile

Shashikant Kumar Skills & Research Interests

Density functional theory

Real space Density functional theory

Nanomaterials

Scientific ML

Top articles of Shashikant Kumar

Title	Journal	Author(s)	Publication Date
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations	arXiv preprint arXiv:2404.07961	Lucas R Timmerman Shashikant Kumar Phanish Suryanarayana Andrew J Medford	2024/4/11
On-the-fly machine learned force fields for the study of warm dense matter: application to diffusion and viscosity of CH	arXiv preprint arXiv:2402.13450	Shashikant Kumar Xin Jing John E Pask Phanish Suryanarayana	2024/2/21
Review of the second charged-particle transport coefficient code comparison workshop	Physics of Plasmas	Lucas J Stanek Alina Kononov Stephanie B Hansen Brian M Haines SX Hu ...	2024/5/1
On the bending of rectangular atomic monolayers along different directions: an ab initio study	Nanotechnology	Shashikant Kumar Phanish Suryanarayana	2023
Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning	The Journal of Chemical Physics	Shashikant Kumar Xin Jing John E Pask Andrew J Medford Phanish Suryanarayana	2023/12/28
Version 2.0. 0--SPARC: Simulation Package for Ab-initio Real-space Calculations	arXiv preprint arXiv:2305.07679	Boqin Zhang Xin Jing Qimen Xu Shashikant Kumar Abhiraj Sharma ...	2023/5/12
Version 2.0. 0-M-SPARC: Matlab-Simulation Package for Ab-initio Real-space Calculations	SoftwareX	Boqin Zhang Xin Jing Shashikant Kumar Phanish Suryanarayana	2023/2/1
Accurate parameterization of the kinetic energy functional	The Journal of Chemical Physics	Shashikant Kumar Edgar Landinez Borda Babak Sadigh Siya Zhu Sebastian Hamel ...	2022/1/14
Flexoelectricity in atomic monolayers from first principles	Nanoscale	Shashikant Kumar David Codony Irene Arias Phanish Suryanarayana	2021
Bending moduli for forty-four select atomic monolayers from first principles	Nanotechnology	Shashikant Kumar Phanish Suryanarayana	2020/8/5
On preconditioning the self-consistent field iteration in real-space density functional theory	Chemical Physics Letters	Shashikant Kumar Qimen Xu Phanish Suryanarayana	2020/1/1