Using real-valued multi-objective genetic algorithms to model molecular absorption spectra and Raman excitation profiles in solution
Literature Information
Margaret H. Hennessy, Anne Myers Kelley
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.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.














