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Progress in theoretical chemistry and physics

Non-Adiabatic Molecular Dynamics and Quantum Solvent Effects

Oleg V. Prezhdo, Craig Brooksby

Published Jan 1, 2003• 1 citation
Publisher
DOI
10.1007/978-94-017-0635-3_12

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We propose a mixed quantum-classical hydrodynamic framework to model short-time inertial effects in the non-adiabatic evolution of a quantum solute coupled to a classical polar solvent. Drawing upon the work of Burghardt and Bagchi [Chem. Phys. 329 (2006), 343], we employ the Hamiltonian approach to incorporate consistent backreaction and preserve quantum decoherence beyond standard Ehrenfest dynamics. The solvent is treated as an ideal polar fluid and the quantum solute state is coupled to both the position and molecular orientation coordinates of the liquid. This approach retains essential solute-solvent correlations while reducing the computational complexity of previous approaches. We further incorporate dissipative terms to capture both inertial effects and polarization relaxation. After establishing the general setting for non-local dielectric continua, the Marcus local approximation is integrated into the model thereby extending traditional solvation theory to account for collective fluid sloshing on fast timescales.
Nonlinear optical properties of a two-level molecular system immersed in a thermal bath have been studied in the present work. Solvent effects were explicitly considered by modeling the non-radiative interaction with the solute as a random variable. The innovation of this treatment is that it allows us to take into account the environment, inducing quantum effects not considered by classical treatment. The major contribution of the methodology proposed in this work, is the implementation of an approximant to the Voigt function as a probability distribution, because it allow us to cover a wider range of possible interactions among the solvent and the molecular system by simple changing the parameters [Formula: see text] and [Formula: see text], associated to the variances of the Lorentzian and Gaussian distributions, respectively.