Cycle/Years
39th cycle [2023-2026]
Contacts
Supervisors
Prof. Benedetta Mennucci and Dr. Lorenzo Cupellini
Title of the PhD project
Machine Learning strategies for excited-state dynamics of biological systems
Abstract of the PhD project
The intricate response of biological systems to light involves complex factors operating across diverse spatial and temporal scales. Events at the excited state of small chromophores within photo-responsive proteins can induce profound structural changes, necessitating a multi-scale approach. This entails integrating the precision of quantum mechanical (QM) descriptions with rapid evaluations of molecular properties, especially within intricate environments.
In this project, our specific focus lies on a process known as non-photochemical quenching (NPQ), frequently occurring in photosynthetic systems, where numerous chromophores are involved in energy or charge transfer processes. Efficiently computing their properties, interactions, and incorporating environmental effects at a low computational cost is pivotal for this research.
In this project, we investigate Machine Learning (ML)-based strategies to achieve this goal, combining statistical learning methods with a detailed understanding of the systems at hand. This approach promises efficient models for predicting biological functions of interest, also paving the way for performing excited state dynamics simulations at a lower computational cost.
Academic Fields and Disciplines (SSD), main and secondary
CHIM02
Publications
• A. Arcidiacono, E. Cignoni, P. Mazzeo, L, Cupellini, B. Mennucci, Predicting Solvatochromism of Chromophores in Proteins with QM/MM and Machine Learning, (2024), Journal of Physical Chemistry A, 128, 18, 3646-3658, DOI:10.1021/acs.jpca.4c00249
• A. Arcidiacono, D. Accomasso, L. Cupellini, B. Mennucci, How orange carotenoid protein controls the excited state dynamics of canthaxanthin, (2023), Chemical Science, 14, 11158-11169, DOI:10.1039/D3SC02662K
Poster communications at conferences
• A. Arcidiacono, E. Cignoni, P. Mazzeo, L, Cupellini, B. Mennucci, Predicting solvatochromism of carotenoids with Machine Learning, Chemistry for the Future 2024, Dipartimento di Chimica e Chimica Industriale, Università di Pisa (Italy), 3-6 June 2024.
• A. Arcidiacono, D. Accomasso, L. Cupellini, B. Mennucci, The photochemical mchanism of the activation of Orange Carotenoid Protein, 1st ViRAPID Workshop 2024: Bringing together rare event sampling, excited state dynamics and machine learning, Univeristy of Vienna (Austria), 26-29 February 2024.
• A. Arcidiacono, D. Accomasso, L. Cupellini, B. Mennucci, The photochemical mchanism of the activation of Orange Carotenoid Protein, VIII Congresso Nazionale della Divisione di Chimica Teorica e Computazionale della SCI, Scuola Normale Superiore, Pisa (Italy), 20-22 September 2023.
• A. Arcidiacono, D. Accomasso, L. Cupellini, B. Mennucci, The photochemical mchanism of the activation of Orange Carotenoid Protein, Chemistry for the Future 2023, Dipartimento di Chimica e Chimica Industriale, Università di Pisa (Italy), 28-30 June 2023.
Funding
PRIN 2022 grant from Italian MUR.
