Using real-valued multi-objective genetic algorithms to model molecular absorption spectra and Raman excitation profiles in solutiony

The empirical modeling of the absorption spectra and resonance Raman excitation profiles of a large molecule in solution requires adjustment of a minimum of dozens of parameters to fit several hundred data points. This is a difficult optimization problem because all of the observables depend on all of the parameters in a highly coupled and nonlinear manner. Standard nonlinear least-squares fitting methods are highly susceptible to becoming trapped in local minima in the error function unless very good initial guesses for the molecular parameters are made. Here, we demonstrate a method that employs a real-valued genetic algorithm to force a broad search through parameter space to determine the best-fit parameters. The multiobjective genetic algorithm is successful at inverting absorption spectra and Raman excitation profiles to determine molecular parameters. When vibronic structure is evident in the absorption profile, the algorithm returns nearly quantitative results. For broad, featureless profiles, the algorithm returns the correct slope of the excited state surface but cannot independently determine the excited-state frequency and the equilibrium geometry change. Compared with manual adjustment of parameters to obtain a best fit, the genetic algorithm is computationally less efficient but requires less human time.