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Giovanni M. Pavan (Politecnico di Torino) ABSTRACT: Nature uses self-assembly to generate innately dynamic complex molecular systems. Learning how to rationally design artificial systems with similar behaviors[1] would be a breakthrough. But the microscopic details of their dynamic behavior are difficult to ascertain. Molecular models,[2,3] simulations[4,5] and machine learning[6,7] are fundamental achieve such ambitious goal. In this talk, I show examples of how the intrinsic dynamics of self-assembling systems determine the collective ensemble properties emerging within them. I will show our recent efforts to unravel the intricate communication networks present in complex molecular systems of different types,[8-9] and for tracking dominant fluctuations that are key for the collective properties that emerge within them.[8-13] The data-driven methods that we developed and the approaches we are using to this end are general, and can be applied also to study experimentally resolved trajectories of micro- and macroscopic complex dynamical systems whose physics is unknown a priori.[11,12] This opens interesting questions on, e.g., at what level/scales complex behaviors emerge in self-organizing systems and how, which may be relevant in many fields. The results that we are obtaining are general, and are challenging how we look at complex self-organizing systems and at a variety of phenomena that are central in materials science.[8-15] 1. T. Aida, E. W. Meijer, S. I. Stupp; Science, 2012, 335, 813 2. D. Bochicchio, G. M. Pavan; ACS Nano, 2017, 1, 1000 3. A. L. de Marco, D. Bochicchio, A. Gardin, G. Doni, G. M. Pavan; ACS Nano, 2021, 15, 14229 4. D. Bochicchio, M. Salvalaglio, G. M. Pavan; Nature Commun., 2017, 8, 147 5. L. Leanza, C. Perego, L. Pesce, M. von Delius, M. Salvalaglio, G. M. Pavan; Chem. Sci. 2023, 14, 6716 6. A. Gardin, C. Perego, G. Doni, G. M. Pavan; Commun. Chem., 2022, 5, 82 7. M. Crippa, A. Cardellini, M. Cioni, G. Csányi, G. M. Pavan; Mach. Learn. Sci. Technol., 2023, 4, 045044 8. M. Crippa, C. Perego, A. L. de Marco, G. M. Pavan; Nature Commun., 2022, 13, 2162 9. C. Lionello, C. Perego, G. M. Pavan.; ACS Nano, 2023, 17, 275 10. M. Crippa, A. Cardellini, C. Caruso, G. M. Pavan; PNAS, 2023, 120, e2300565120 11. M. Becchi, F. Fantolino, G. M. Pavan; PNAS, 2024, 121, e2403771121 12. C. Caruso, Crippa, Cardellini, Cioni, Perrone, Delle Piane, G. M. Pavan; PNAS Nexus 2025, 4(2), pgaf038 13. M. Crippa, C. Perego, G. M. Pavan; ChemRxiv 2024, DOI:10.26434/chemrxiv-2024-cc0bl 14. M. Cioni, D. Polino, D. Rapetti, L. Pesce, M. Delle Piane; J. Chem Phys., 2023, 158, 124701 15. M. Cioni, Delle Piane, Polino, Rapetti, Crippa, Arslan, VanAert, Bals, G.M. Pavan; Adv. Sci., 2024, 2307261 |
CMSP Seminar (Atomistic Simulation Seminar Series): From microscopic dynamics to emergent behaviors in complex self-organizing molecular & supramolecular systems
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