Zilvinas Rinkevicius, Xin Li, Olav Vahtras, Manuel Brand, Karan Ahmadzadeh, Magnus
Ringholm, Nanna List, and Patrick Norman
With the ease of Python library modules, VeloxChem offers a front end to quantum chemical
calculations on contemporary high-performance computing (HPC) systems and aims at
harnessing the future compute power within the EuroHPC initiative. At the heart of this
software lies a module for the evaluation of electron-repulsion integrals (ERIs) using the ObaraSaika recurrence scheme, where a high degree of efficiency is achieved by employing
architecture-independent vectorization via OpenMP SIMD pragmas in the auto- generated C++
source code. The software is topology aware and with a Python-controlled work and task flow,
the idle time is minimized using an MPI/OpenMP partitioning of resources.
In the second software layer, we have implemented a highly accurate SCF start guess based
on atomic densities and a first-level of iterations in a reduced version of the user-defined basis
set, leading to a very smooth convergence in the subsequent standard DIIS scheme. This layer
also includes vectorized and OpenMP/MPI parallelized modules for efficient generation of DFT
grid points and weights as well as kernel integration.
In the third software layer, we present real and complex response functions as to address
dispersive and absorptive molecular properties in spectroscopy. The kernel module in this layer
is the iterative linear response equation solver that we have formulated and implemented for a
combination of multiple optical frequencies and multiple perturbation operators. With efficient
use of computer memory, we enable the simultaneous reference to, and solving of, in the order
of 1,000 response equations for sizable biochemical systems without spatial symmetry, and we
can thereby determine electronic response spectra in arbitrary wavelength regions, including
UV/vis and X-Ray, without resolving the sometimes embedded excited states in the spectrum.
E.g. the electronic CD spectrum (involving the Cartesian sets of electric and magnetic
perturbations) over a range of some 10 eV is obtained at a computational cost comparable to
that of determining the transition energy of the lowest excited state, or optimizing the electronic
structure of the reference state.
A new and efficient Python/C++ modular library for real and complex response functions at the
level of Kohn-Sham density functional theory