logo SBA

ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-01172024-143606


Thesis type
Tesi di dottorato di ricerca
Author
CIGNONI, EDOARDO
URN
etd-01172024-143606
Thesis title
Statistical Learning Strategies for the Modelling of Light Harvesting Complexes
Academic discipline
CHIM/02
Course of study
SCIENZE CHIMICHE E DEI MATERIALI
Supervisors
tutor Prof.ssa Mennucci, Benedetta
relatore Dott. Cupellini, Lorenzo
Keywords
  • excited states
  • hamiltonian learning
  • light harvesting
  • machine learning
  • molecular dynamics
  • nonphotochemical quenching
Graduation session start date
02/02/2024
Availability
Full
Summary
Modeling the conformational and excited-state dynamics in light-harvesting complexes requires an integrated multiscale computational approach. Molecular dynamics is needed to obtain a good conformational sampling of these complexes, while quantum chemical calculations enable the study of excited state processes. Here we have complemented these techniques with machine learning in multiple ways. First, machine learning was used to aid the interpretation of the complex conformational landscape obtained from molecular dynamics simulations of light-harvesting complexes. In addition, we have built regression models predicting the excited state properties of the embedded pigments, in a polarizable electrostatic environment, bypassing the computational burden of quantum chemical calculations.We show that machine learning techniques are powerful computational instruments for the study of these and other complex biomacromolecules.
File