PARSEC

PARSEC is a computer code that solves the Kohn-Sham equations by expressing electron wave-functions directly in real space, without the use of explicit basis sets. It uses norm-conserving pseudopotentials (Troullier-Martins and other varieties). It is designed for ab initio quantum-mechanical calculations of the electronic structure of matter, within density-functional theory. PARSEC is optimized for massively parallel computing environment, but it is also compatible with serial machines. A finite-difference approach is used for the calculation of spatial derivatives. Owing to the sparsity of the Hamiltonian matrix, the Kohn-Sham equations are solved by direct diagonalization, with the use of extremely efficient sparse-matrix eigensolvers. Some of its features are: Choice of boundary conditions: periodic (on all three directions), or confined. Structural relaxation. Simulated annealing. Langevin molecular dynamics. Polarizability calculations (confined-system boundary conditions only). Spin-orbit coupling. Non-collinear magnetism.


References in zbMATH (referenced in 17 articles )

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  1. Bodroski, Zarko; Vukmirović, Nenad; Skrbic, Srdjan: Gaussian basis implementation of the charge patching method (2018)
  2. Duersch, Jed A.; Shao, Meiyue; Yang, Chao; Gu, Ming: A robust and efficient implementation of LOBPCG (2018)
  3. Ghosh, Swarnava; Suryanarayana, Phanish: SPARC: accurate and efficient finite-difference formulation and parallel implementation of density functional theory: extended systems (2017)
  4. Li, Ruipeng; Xi, Yuanzhe; Vecharynski, Eugene; Yang, Chao; Saad, Yousef: A thick-restart Lanczos algorithm with polynomial filtering for Hermitian eigenvalue problems (2016)
  5. Wen, Zaiwen; Yang, Chao; Liu, Xin; Zhang, Yin: Trace-penalty minimization for large-scale eigenspace computation (2016)
  6. Xi, Yuanzhe; Saad, Yousef: Computing partial spectra with least-squares rational filters (2016)
  7. Banerjee, Amartya S.; Elliott, Ryan S.; James, Richard D.: A spectral scheme for Kohn-Sham density functional theory of clusters (2015)
  8. Liu, Xin; Wen, Zaiwen; Zhang, Yin: An efficient Gauss-Newton algorithm for symmetric low-rank product matrix approximations (2015)
  9. Zhou, Yunkai; Chelikowsky, James R.; Saad, Yousef: Chebyshev-filtered subspace iteration method free of sparse diagonalization for solving the Kohn-Sham equation (2014)
  10. Di Napoli, Edoardo; Berljafa, Mario: Block iterative eigensolvers for sequences of correlated eigenvalue problems (2013)
  11. Fang, Jun; Gao, Xingyu; Zhou, Aihui: A symmetry-based decomposition approach to eigenvalue problems (2013)
  12. Fang, Jun; Gao, Xingyu; Zhou, Aihui: A finite element recovery approach to eigenvalue approximations with applications to electronic structure calculations (2013)
  13. Fang, Jun; Gao, Xingyu; Zhou, Aihui: A Kohn-Sham equation solver based on hexahedral finite elements (2012)
  14. Soba, Alejandro; Bea, Edgar Alejandro; Houzeaux, Guillaume; Calmet, Hadrien; Cela, José María: Real-space density functional theory and time dependent density functional theory using finite/infinite element methods (2012)
  15. Sidje, Roger B.; Saad, Yousef: Rational approximation to the Fermi-Dirac function with applications in density functional theory (2011)
  16. Rizea, M.; Ledoux, V.; Van Daele, M.; Vanden Berghe, G.; Carjan, N.: Finite difference approach for the two-dimensional Schrödinger equation with application to scission-neutron emission (2008)
  17. Zhou, Yunkai; Saad, Yousef: Block Krylov-Schur method for large symmetric eigenvalue problems (2008)