Cycle/Years
39th cycle [2023-2026]
Contacts
Supervisors
Dr. Lorenzo Cupellini and Prof. Benedetta Mennucci
Title of the PhD project
Simulation of exciton dynamics in photosynthetic supercomplexes
Abstract of the PhD project
Photosynthesis is probably one of the most important natural processes, powering, directly or indirectly, all life on Earth. The photosynthetic machinery is able to absorb energy from sunlight and transform it into chemical bonds of organic compounds. Such a delicate task is accomplished by means of biological antennas, complexes in which pigments are coordinated in high concentrations to a protein matrix which finely tunes their optical properties. In most photosynthetic organisms, antennas are grouped into modular architectures called supercomplexes, whose working details still remain elusive.
Recently resolved structures and new time-resolved spectroscopic studies are opening new questions about the excited state dynamics inside supercomplexes. Some suggest that structural modifications of the architecture interfaces may regulate the energy transfer process, determining the rate and efficiency with which the harvested light reaches the photosynthetic reaction centers.
The aim of this PhD project is to shed light on the present open questions by building a complete theoretical model for light-harvesting in photosynthetic supercomplexes. This requires developing new computational strategies, likely involving synergy of traditional QM/MM and most recently developed ML approaches, in order to overcome challenges due to the large dimension and complex nature of the system. A robust functional model of natural architectures may pave the way for future applications in artificial energy conversion.
Academic Fields and Disciplines (SSD), main and secondary
CHIM02
Publications
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, Insights into Energy Transfer in Light-Harvesting Complex II Through Machine-Learning Assisted Simulations, (2024), J. Phys. Chem. B, 128, 21, 5188–5200, https://dx.doi.org/10.1021/acs.jpcb.4c01494
Oral communications at conferences
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, “How machine learning enhances simulation of energy transfer in light-harvesting complex II”, 1st ViRAPID Workshop 2024, University of Vienna, 26-29/02/24.
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, “Simulation of energy transfer pathways and transient absorption in major light-harvesting complex LHCII of plants”, DCP24, Dynamics and Complexity Pisa, 8/06/24.
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, “How machine learning enhances simulation of energy transfer in light-harvesting complex II”, CFF2024, Chemistry for the Future, University of Pisa, 3-5/07/24.
Poster communications at conferences
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, “How machine learning enhances simulation of energy transfer in light-harvesting complex II”, CFF2024, Chemistry for the Future, University of Pisa, 3-5/07/24.
• E. Betti, P. Saraceno, E. Cignoni, L. Cupellini, B. Mennucci, “How machine learning enhances simulation of energy transfer in light-harvesting complex II”, XXVIII Congresso Nazionale della Società Chimica Italiana, Milan, 26-31/07/24.
Other achievements
• Poster award by Royal Society of Chemistry, “How machine learning enhances simulation of energy transfer in light-harvesting complex II”, XXVIII Congresso Nazionale della Società Chimica Italiana, Milan, 26-31/07/24.
