and {γ -1 α } are the vibrational lifetimes. The solvent spectral density can be adequately modeled via a Drude-Lorentz functional form J 0 (ω) = 2 π λ 0 ωΛ ω 2 +Λ 2 which arises from J α (ω) when the lifetimes are short (i.e. when ω α /γ α ≪ 1). Thus, all the parameters defining the spectral density can be extracted from the RR experiments. While the specific value of γ α cannot be directly resolved using RR, its value plays no significant role in electronic decoherence as vibrational relaxation occurs on much longer time scales for physical values of ℏγ α ∈ (1, 20)cm -1 (see Fig. ). The spectral densities that can be extracted from RR are due to "pure dephasing" processes where there is no net exchange of energy between the system and the environment. That is, for processes in which the environment does not lead to electronic transitions or relaxation. In molecules, these pure dephasing processes have been the subject of intense interest as they typically occur in time scales that are much faster than the overall relaxation and dominate the decoherence dynamics. Naturally, this J(ω) reconstruction is limited by experimental constraints and the scope of validity of the displaced harmonic oscillator model. For instance, internal conversion due to conical intersections occurring on time scales comparable or faster than the dephasing, or unresolvable electronic state congestion, will invalidate the analysis. In addition, anharmonicities, frequency shifts and Duschinsky rotations (mode mixing) in the potential energy surfaces can introduce additional time scales to the decoherence that are beyond the displaced harmonic oscillator model. However, numerical evidence thus far suggest that their influence occurs on time scales that are much slower than the overall dephasing in the condensed phase and thus they are expected to play a minor role. DECOHERENCE PATHWAYS Once a spectral density is extracted from RR experiments, the task is then to use it to capture the realistic quantum dynamics of the molecular chromophore. The challenge is that the computational cost of doing so increases exponentially with the number of features N + 1 in the environment.