Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning
Dongyu Lyu, Matthias Holzenkamp, Vivin Vinod, Yannick Marcel Holtkamp,, Sayan Maity, Carlos R. Salazar, Ulrich Kleinekath\"ofer, Peter Zaspel

TL;DR
This study combines multiscale quantum mechanics and multifidelity machine learning to simulate excitation energy transfer in a synthetic porphyrin-clay system, revealing its potential as an artificial light-harvesting device.
Contribution
It introduces a novel multifidelity machine learning approach to accurately predict excitation energies in complex dye-surface systems, enhancing computational efficiency.
Findings
MFML predicts excitation energies at DFT level with high accuracy.
Porphyrin-clay systems show efficient excitation energy transfer.
The approach enables large-scale simulations of light-harvesting complexes.
Abstract
Natural light-harvesting antenna complexes efficiently capture solar energy using chlorophyll, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this natural configuration, artificial clay-porphyrin antenna structures have been experimentally synthesized and have demonstrated remarkable excitation energy transfer properties. The study presents the computational design and simulation of a synthetic light-harvesting system that emulates natural mechanisms by arranging cationic free-base porphyrin molecules on an anionic clay surface. We investigated the transfer of excitation energy among the porphyrin dyes using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach based on the semi-empirical density functional-based tight-binding (DFTB) theory for the ground state dynamics. To improve the accuracy of our results, we incorporated an innovative…
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Taxonomy
TopicsWater Quality Monitoring and Analysis · Spectroscopy and Quantum Chemical Studies · Photonic Crystals and Applications
