activity
20172026
most citedDesign and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors

86 citations · 174 across the 29 of their papers we have counts for

collaborators
Showing physics.comp-phShow all

5 papers · 1 filter

physics.comp-ph2026

Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation

Abir Haque, Edmond Chow, Shikhar Shah +3

We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory.…

physics.comp-ph2024

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…

physics.comp-ph2024

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…

physics.comp-ph2023

Version 2.0.0 -- SPARC: Simulation Package for Ab-initio Real-space Calculations

Boqin Zhang, Xin Jing, Qimen Xu +8

SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn-Sham density functional theory calculations. Version 2.0.0 of the s…

physics.comp-ph2020★ 5 cited

SPARC: Simulation Package for Ab-initio Real-space Calculations

Qimen Xu, Abhiraj Sharma, Benjamin Comer +5

We present SPARC: Simulation Package for Ab-initio Real-space Calculations. SPARC can perform Kohn-Sham density functional theory calculations for isolated systems such as molecule…