Data di Pubblicazione:
2021
Citazione:
EMERGENT PHENOMENA IN CONDENSED MATTER, SOFT MATTER AND COMPLEX SYSTEMS / F. Mambretti ; supervisor: D. E. Galli. Dipartimento di Fisica Aldo Pontremoli, 2021 Mar 24. 33. ciclo, Anno Accademico 2020. [10.13130/mambretti-francesco_phd2021-03-24].
Abstract:
Physical systems composed of a large number of reciprocally interacting constituents provide the natural context for the rise of emergent phenomena. Despite the intrinsic difficulty in providing a mathematical definition of what is meant for ‘emergence’ (see [Baas, in Langton, Alife III, Santa Fe Studies in the Sciences of Complexity, Proc. Volume XVII, Addison-Wesley, (1994)]), the intuitive notion of emergent property is that of a collection of interact- ing objects showing a novel collective behavior, qualitatively different from and not immediately attributable to the behaviors of the individual components. Non-linear interactions among elements of the system, or interactions between the system and the environment, or merely the large number of constituents are usually the motivations addressed to be responsible for emergent behavior. It is important to remark that emergent properties can only be inferred from a comprehension of the collective properties of the microscopic constituents [Kivelson et al, npj Quant. Mater. 1, 16024 (2016)].
In this regard, computer simulations provide a unique tool to support experimental observation, develop abstract models and investigate systems’ properties at a microscopic level. In general, condensed matter, particularly soft matter but also the complex systems studied in Physics, are necessarily described via simplified models, which include the key features of the corresponding real systems. On the one hand, this certainly represents a powerful approach when it finds its roots in the concept of universality, connected with critical phenomena, but this also turns into a limiting factor for the realistic description of the considered phenomena. On the other hand, it makes the properties of such abstract simulated systems calculable and investigable via computer simulations. As a consequence, the simulations assume a key role in complementing the comparison between experiments and theory [Frenkel and Smit, Understanding Molecular Simulations, Academic Press (2002); Allen and Tildesley, Computer simulation of liquids, Oxford University Press (2017)]. In this sense, simulations are often regarded as being computer experiments, in which materials properties and novel phases of matter can be investigated.
The present PhD thesis is a collection of the main results coming from four different research lines which I have been involved into in the last 3 years. The topics could appear to be rather diverse but they are all connected by the presence of emergent phenomena which were studied via computer simulations (Molecular Dynamics and Monte Carlo methods, mainly). Three of these four research lines are related to collaborations with as many experimental groups. The first group I started collaborating with is led by dr. R. Grisenti, at the University of Frankfurt (https://www.atom. uni-frankfurt.de/hhng-grisenti/index.html). As reported in Chapter 1 and in a recent paper which I contributed to as first co-author [Schottelius, Mambretti et al., Nat. Mat. (2020)], we studied the crystal growth of supercooled Ar–Kr liquid mixtures by means of a micro–jet experiment, Molecular Dynamics simulation and thermodynamic analysis. The second ongoing collaboration is with the group of prof. P. Milani, which is the leader of the CIMaINa laboratories (http://cimaina.unimi.it/) at the Università degli Studi di Milano. We developed an abstract stochastic model of resistive switching devices that they are characterizing for neuromorphic applications (see Chapter 3). More recently, I started a collaboration with the group led by prof. T. Bellini at the Università degli Studi di Milano (https://sites.google.com/site/unimisoft/), in order to investigate the spinodal decomposition of mixtures of DNA nanost
Tipologia IRIS:
Tesi di dottorato
Elenco autori:
F. Mambretti
Link alla scheda completa: