###Machine learning of quantum dynamics, spectroscopy and quantum optics of biomolecules
Project ID: 2228bd1096 (You will need this ID for your application)
Research Theme: Physical Sciences
UCL Lead department: Physics and Astronomy
Lead Supervisor: Alexandra Olaya-Castro
Project Summary:
Characterising and predicting the physical behaviour of biomolecules, such as the supramolecular complexes that absorb photons and transfer energy in photosynthetic organisms, is of fundamental importance to gain a deep understanding and possible control of the quantum processes taking place at the nanoscale regime of biology [1].
Time-dependent spectroscopic signals resulting from the interaction between light and ensembles of biomolecules are used as diagnostic tool for the underlying quantum dynamical behaviour of the systems of interest. However, the interpretation of such signals is challenging due to a variety of factors including the heterogeneity of the biomolecules in the ensemble, the high dimensionality of the spectroscopic data, and the intricate electronic and vibrational interactions within a biomolecule.
Developing efficient and robust methods that map complex time-dependent spectra onto physical models is of key importance for the application of such techniques to increasingly complex biosystems. In this project we will investigate the potential of machine learning for addressing this challenge.
Machine learning involves the use of algorithms for data processing to predict the behaviour of a complex system [2]. Recently, it has been proposed as a tool to predict unknown parameters in the physical models underlying measured spectra [3,4]. The main goal of this project is therefore to implement machine learning algorithms, such as supervised neuronal networks [2,3], for efficient prediction of disorder-averaged spectra and dynamics of biomolecules.
This project is strongly computational. Through this research the student will learn theory and computational approaches to open quantum systems as well as biophysics of photosynthetic biomolecules.
[1] E J O’Reilly and A Olaya-Castro, Nat Commun 5, 3012 (2014) [2] G Carleo et al., Rev. Mod. Phys. 91, 045002 (2019) [3] M Rodríguez, T. Kramer, Chem. Phys. 520, 52-60 (2019) [4] K A Parker et al., J. Phys. Chem. Lett. 13, 7454−7461 (2022)