Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning

Abstract

Natural light-harvesting antenna complexes efficiently capture solar energy mostly using chlorophyll molecules, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this natural configuration, artificial clay-porphyrin antenna structures have experimentally been synthesized and demonstrated to exhibit remarkable excitation energy transfer properties. The current study presents a computational design and simulations of a synthetic light-harvesting system that emulates natural mechanisms by arranging cationic free-base porphyrin molecules on an anionic clay surface. The transfer of excitation energy among the porphyrin dyes is investigated using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach based on the semi-empirical density functional-based tight-binding theory for the ground state dynamics. To improve the accuracy of the results, an innovative multifidelity machine learning approach is incorporated, which allows the prediction of excitation energies at the numerically demanding time-dependent density functional theory level together with the def2-SVP basis set. This approach is applied to an extensive dataset of 640 k geometries for the 90-atom porphyrin structures, facilitating a thorough analysis of the excitation energy diffusion among the porphyrin molecules adsorbed to the clay surface. The insights gained from this study, inspired by natural light-harvesting complexes, demonstrate the potential of porphyrin-clay systems as effective energy transfer systems.