Molecular properties of large systems based on the damped response theory approach
||Molecular properties of large systems based on the damped response theory approach|
||Joanna Kauczor <firstname.lastname@example.org>|
||2014-05-01 – 2015-05-01|
Molecular properties for ground and excited states, can be determined from so-called molecular response functions and their poles and residues. In standard response theory, absorption spectra are obtained by solving a generalized eigenvalue problem. This computationally expensive iterative procedure starts from the energies of the lowest excited states, and only these lowest excitations are addressed. In consequence, it may not be possible to access all excitation energies of interest using this approach. The straightforward comparison between standard theory and experiment is therefore impossible in many interesting regions of the spectrum, e.g. the X-ray absorption region. This problem can be solved by using the complex polarization propagator approach , also known as damped (complex) response theory.
In the past years, there has been interest in the development of an efficient solver for solving damped response equations, namely a complex polarization propagator solver (also in a linearly scaled fashion) at the Hartree-Fock and Kohn-Sham density functional level of theory. New solver, namely: the algorithm with symmetrized vectors  was developed and is implemented in DALTON2013 , a molecular electronic structure program, as well as LSDALTON , its linearly scaled counterpart. It may be used when dispersion coefficients , one-photon absorption, electronic circular dichroism, magnetic circular dichroism  and X-ray absorption spectra  are calculated using the complex polarization propagator approach. It is also possible to perform calculations of absorption spectra for large organic and biological systems in the presence of a solvent. When calculations of spectrum are performed in a chosen frequency interval, all response equations are solved simultaneously, greatly reducing the calculation time, however also increasing the memory requirements. The code has been modularized and can be exploited as an equation solver in other programs.
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