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We analyze how to obtain non-resonant and resonant Raman spectra within the Placzek as well as the Albrecht approximation. Both approximations are derived from the matrix element for light scattering by application of the Kramers, Heisenberg and Dirac formula. It is shown that the Placzek expression results from a semi-classical approximation of the combined electronic and vibrational transition energies. Molecular hydrogen, water and butadiene are studied as test cases. It turns out that the Placzek approximation agrees qualitatively with the more accurate Albrecht formulation even in the resonant regime for the excitations of single vibrational quanta. However, multiple vibrational excitations are absent in Placzek, but can be of similar intensities as single excitations under resonance conditions. The Albrecht approximation takes multiple vibrational excitations into account and the resulting simulated spectra exhibit good agreement with experimental Raman spectra in the resonance region as well.
We describe a simplified approach to simulating Raman spectra using ab initio molecular dynamics (AIMD) calculations. Our protocol relies on on-the-fly calculations of approximate molecular polarizabilities using a sum over orbitals (as opposed to states) method.
We present a new method to compute resonance Raman spectra based on ab initio level calculations using the frequency-dependent Placzek approximation. We illustrate the efficiency of our hybrid quantum-classical method by calculating the Raman spectra
We perform on-the-fly non-adiabatic molecular dynamics simulations using the symmetrical quasi-classical (SQC) approach with the recently suggested molecular Tully models: ethylene and fulvene. We attempt to provide benchmarks of the SQC methods usin
Strong magnetic fields have a large impact on the dynamics of molecules. In addition to the changes of the electronic structure, the nuclei are exposed to the Lorentz force with the magnetic field being screened by the electrons. In this work, we exp
The concept of machine learning configuration interaction (MLCI) [J. Chem. Theory Comput. 2018, 14, 5739], where an artificial neural network (ANN) learns on the fly to select important configurations, is further developed so that accurate ab initio