D. Lyu, V. Vinod, M. Holzenkamp, Y. M. Holtkamp, S. Maity, C. R. Salazar, U. Kleinekathöfer, P. Zaspel. Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning. Adv. Theory Simul., e00271, 2025. DOI: 10.1002/adts.202500271; also available as arXiv.2410.20551.
Our research group is excited to announce the publication of our latest paper, “Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning,” in the journal Advanced Theory and Simulations. This work, authored by Dongyu Lyu, Vivin Vinod, Matthias Holzenkamp, Yannick Marcel Holtkamp, Sayan Maity, Carlos R. Salazar, Ulrich Kleinekathöfer, and Peter Zaspel, marks a significant advancement in computational chemistry and effective use of multifidelity methods in machine learning.
The study presents an application of understanding excitation energy transfer within complex synthetic light-harvesting systems. Inspired by nature’s efficient mechanisms, the research focuses on modeling the intricate interactions of 90-atom porphyrin molecules arranged on an anionic clay surface.
Key Highlights of the Research:
- High-Accuracy Modeling of Large Systems: The team successfully developed a computational framework to accurately model a large system, processing an extensive dataset of 640,000 molecular geometries. This was achieved while maintaining high-level quantum chemical precision, specifically reaching Density Functional Theory (DFT) accuracy with the def2-SVP basis set.
- Revolutionizing Efficiency with Multifidelity Machine Learning (MFML): A central innovation of this work is the strategic integration of a novel multifidelity machine learning approach. This method dramatically enhanced computational efficiency, yielding over 800x time savings compared to conventional high-fidelity calculations. By optimally leveraging data from multiple levels of theoretical fidelity, the MFML approach made the exploration of such a vast chemical space computationally feasible.
- Insights into Energy Transfer: The findings provide crucial insights into the fundamental processes of excitation energy transfer among porphyrin dyes, demonstrating the immense potential of porphyrin-clay systems for energy applications.
This publication underscores our group’s commitment to pushing the boundaries of computational modeling to solve complex challenges in materials science and energy research. We invite you to explore the full details of this pioneering work.