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SUMMARY:Talk – From microscopic dynamics to dominant fluctuations to com
 plex emergent behaviors in self-organizing matter
DTSTART:20250612T170000
DTEND:20250612T180000
DTSTAMP:20260526T042840Z
UID:5761213a7c64e6813b6824e7246da9a2692efbd48ca39c1e9b7097e9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Giovanni M. Pavan\nNature uses self-assembly to generate
  innately dynamic complex molecular systems. Learning how to rationally de
 sign artificial systems with similar behaviors[1] would be a breakthrough.
  But the microscopic details of their dynamic behavior are difficult to as
 certain. Molecular models\,[2\,3] simulations[4\,5] and machine learning[6
 \,7] are fundamental to achieve such ambitious goal.\nIn this talk\, I sho
 w examples of how the intrinsic dynamics of self-assembling systems determ
 ine the collective ensemble properties emerging within them. I will show o
 ur efforts to unravel the intricate dynamical communication networks prese
 nt in complex molecular and supramolecular systems\,[8-9] and for tracking
  dominant fluctuations that are key for the collective properties that eme
 rge within them.[8-13] The data-driven methods that we are developing to t
 his end are general\,[10-13] and can be applied also to study experimental
 ly resolved trajectories of micro- and macroscopic complex dynamical syste
 ms whose physics is unknown a priori.[13\,14] This opens interesting quest
 ions on\, e.g.\, at what level/scales do complex behaviors emerge in self-
 organizing systems\,[15] which may be relevant in many fields. The results
  that we are obtaining are challenging\, in general\, how we look at a var
 iety of phenomena and problems central in materials science.[8-17]\n \n1.
  T. Aida\, E. W. Meijer\, S. I. Stupp\; Science\, 2012\, 335\, 813\n2. D. 
 Bochicchio\, G. M. Pavan\; ACS Nano\, 2017\, 1\, 1000\n3. A. L. de Marco\,
  D. Bochicchio\, A. Gardin\, G. Doni\, G. M. Pavan\; ACS Nano\, 2021\, 15\
 , 14229\n4. D. Bochicchio\, M. Salvalaglio\, G. M. Pavan\; Nature Commun.\
 , 2017\, 8\, 147\n5. L. Leanza\, C. Perego\, L. Pesce\, M. von Delius\, M.
  Salvalaglio\, G. M. Pavan\; Chem. Sci. 2023\, 14\, 6716\n6. A. Gardin\, C
 . Perego\, G. Doni\, G. M. Pavan\; Commun. Chem.\, 2022\, 5\, 82\n7. M. Cr
 ippa\, A. Cardellini\, M. Cioni\, G. Csányi\, G. M. Pavan\; Mach. Learn. 
 Sci. Technol.\, 2023\, 4\, 045044\n8. M. Crippa\, C. Perego\, A. L. de Mar
 co\, G. M. Pavan\; Nature Commun.\, 2022\, 13\, 2162\n9. C. Lionello\, C. 
 Perego\, G. M. Pavan.\; ACS Nano\, 2023\, 17\, 275\n10. M. Crippa\, A. Car
 dellini\, C. Caruso\, G. M. Pavan\; PNAS\, 2023\, 120\, e2300565120\n11. M
 . Becchi\, F. Fantolino\, G. M. Pavan\; PNAS\, 2024\, 121\, e2403771121\n1
 2. M. Becchi & G.M. Pavan\; arXiv 2025\, DOI:10.48550/arXiv.2504.12990\n13
 . D. Doria\, S. Martino\, M. Becchi & G. M. Pavan\; arXiv 2024\, DOI:10.48
 550/arXiv.2412.13741\n14. C. Caruso\, Crippa\, Cardellini\, Cioni\, Perron
 e\, Delle Piane\, G. M. Pavan\; PNAS Nexus 2025\, 4(2)\, pgaf038\n15. M. C
 rippa\, C. Perego\, G. M. Pavan\; Nature Commun. 2025\, 10.1038/s41467-025
 -025-60150-4 (in press)\n16. M. Cioni\, D. Polino\, D. Rapetti\, L. Pesce\
 , M. Delle Piane\; J. Chem Phys.\, 2023\, 158\, 124701\n17. Perrone\, Cion
 i\, Delle Piane & G.M. Pavan\; J. Chem. Phys. 2025\, accepted (in press)\,
  DOI:10.48550/arXiv.2410.20999)
LOCATION:CM 1 4 https://plan.epfl.ch/?room==CM%201%204
STATUS:CONFIRMED
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