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