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Complex Systems and Statistical Mechanics

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Research Interests

Recent developments in experimental techniques significantly improved our ability to build and manipulate systems at the nanoscale. The driving force behind these achievements is the enormous impact of artificial nanodevices on modern technologies. They are used, for example, to convert and transfer energy, to perform logical operations, and to store memory in an efficient and compact way. Current strategies to design new nanodevices or enhance their current performance remain largely system-specific and often empirical. This lack of general (i.e. system-independent) guiding principles argely results from the inability of traditional thermodynamics to eal with the effects of strong fluctuations, which are ubiquitous in nanodevices, and with the fact that these systems often operate far from equilibrium. Our goal is to further develop the newly discovered theory, called stochastic thermodynamics, which incorporates these characteristic features of nanodevices.

Our research currently follows three main directions. First, we want to incorporate notions of information, computation, feedback and control in the theory of stochastic thermodynamics. This will allow us to address issues related to the performance of information processing (such as copying and erasing information) in finite time. Second, we want to extend stochastic thermodynamics to properly describe the quantum effects which arise when low temperatures and small system sizes are considered. To do so, key thermodynamic concepts such as entropy, entropy production, and detailed balance need to be appropriately defined in terms of the central quantities describing transport in nonequilibrium quantum systems. Finally, we want to demonstrate that stochastic thermodynamics is a suitable tool to study the properties of the natural “nanodevices” fueling the activity of biological cells. We want to use this theory to study, from a physics standpoint, the basic mechanisms by which biochemical networks and the molecular machinery of the cell (e.g. enzymes, molecular motors) operate. A better understanding of the strategies that natural selection has found to operate efficiently and reliably far from equilibrium and in the highly fluctuating environment of the cell could prove very useful to engineer more efficient artificial nanodevices.

Our research strategy consists in proposing model systems that can be studied using analytical as well as numerical mathematical tools. These models range from simple analytically solvable models, which are used to elaborate new theories, to more realistic models, which can be studied using computer simulations and confronted to experimental results.