Talk – From microscopic dynamics to dominant fluctuations to complex emergent behaviors in self-organizing matter

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 to 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 efforts to unravel the intricate dynamical communication networks present in complex molecular and supramolecular systems,[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 are developing to this end are general,[10-13] and can be applied also to study experimentally resolved trajectories of micro- and macroscopic complex dynamical systems whose physics is unknown a priori.[13,14] This opens interesting questions 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 variety of phenomena and problems central in materials science.[8-17]