Modelling of plant light-harvesting spectra

Abstract

In plants, the life-sustaining process of photosynthesis begins when molecular light-harvesting complexes capture sunlight. In order to understand the role and function of these complexes, they are often characterised with the powerful technique of linear optical spectroscopy. Modelling linear optical spectra allows researchers to improve their understanding of the design principles and quantum mechanical processes in light-harvesting complexes, extract molecular parameters quantitatively, and test new methods for calculating spectra or molecular parameters. In this thesis, we consider various methods for the exact and approximate calculation of absorption- and fluorescence-type linear spectra. We discuss the Exact Stochastic Path Integral Evaluation (PI) method, and several approximate methods—including the Full Cumulant Expansion (FCE), complex time-dependent Redfield (ctR), and Redfield and modified Redfield methods. We systematically describe the accuracy of the approximate methods for calculating the spectra of a chlorophyll dimer system with molecular parameters and system–environment interaction similar to that of plant light-harvesting complexes. From this analysis we found the FCE method to perform best for the calculation of absorption-type spectra and for fluorescence-type spectra when the interpigment coupling is not very strong (we regard couplings greater than 300 per cm as very strong). The ctR method generally performs well for couplings smaller than about 100 per cm, except for the calculation of circular dichroism spectra, and outperforms FCE for the calculation of fluorescence-type spectra when the coupling is very strong. Spectra calculated with the Redfield or modified Redfield methods are often inaccurate—especially when the interpigment couplings are strong or site energy gaps are small. We also found that the quality of fluorescence-type spectra depends crucially on the accurate modelling of the Stokes shift and the equilibrium state. We calculate spectra with the ctR method in order to compare literature Hamiltonians of the light-harvesting complex CP29, and we use particle swarm optimisation (PSO) to determine an improved set of energies (and additional molecular parameters) for this complex. Finally, we show that artificial neural networks can be used to accurately predict linear spectra from molecular parameters, and vice versa.