Attention : désormais les séminaires ont lieu tous les lundis à 10h45 en salle  523 du LPTMC - Tour 12-13 


Mathias Casiulis (LPTMC)

Collective motion in an ideal spin fluid

Collective motion, the macroscopic alignment of velocities in a system of particles, is a key feature of active matter systems. From simple Vicsek-like models to real-life experiments, many different systems seem to feature collective motion, often accompanied by exotic correlations and phase separation properties. These phenomena are however generally observed in non-equilibrium dynamics only, and many models are built up in an ad hoc manner to reproduce experimental data.

In the last few years, some works tried to link the exotic features of active systems to well-known equilibrium dynamics, by studying the low Peclet number limit of active systems for instance [1], thereby finding links between the usual 2d melting transition and the acclaimed MIPS (Motility-Induced Phase Separation), for instance.

The aim of my work is to study, in a toy-model that was designed to be as simple as possible [2], whether collective motion can exist in a conservative framework, when introducing a spin-velocity coupling in a spin fluid. In this talk, I will discuss the model itself, its numerical phenomenology and its links to various other systems and results of statistical mechanics.

[1] L. Cugliandolo, P. Digregorio, G. Gonella, and A. Suma, Phase Coexistence in Two-Dimensional Passive and Active Dumbbell Systems, PRL 119, 268002 (2017)

[2] S. Bore, M. Schindler, K. Lam, E. Bertin, and O. Dauchot, Coupling Spin to velocity: collective motion of Hamiltonian polar particles, J. Stat. Mech. 033305 (2016)


Alexis Poncet (LPTMC)

Tagged particles in single-file systems

Single-file systems, in which particles confined to a channel cannot overtake each other, exhibit a well-known subdiffusive scaling.
This anomalous behavior is a direct consequence of strong spatial correlations induced by the geometrical constraints. Even if this fact has been recognized qualitatively for a long time, up to now there has been no full quantitative determination of these correlations.
In this talk, we present several theoretical approaches that enable us to derive expressions for multiple-tag observables in the Symmetric Exclusion Process (SEP). We will cover the cases of both high-density and arbitrary-density systems, and of both unbiased and biased tagged particles.

Mathieu Salanne (Phénix Jussieu)

Modeling supercapacitors at the molecular scale

The electric double layer is generally viewed as simply the boundary that interpolates between an electrolyte solution and a metal surface. Contrary to that view, recent studies have shown that the interface between ionic liquids and metallic electrodes can exhibit structures and fluctuations that are not simple reflections of surrounding bulk materials [1]. The charge of the electrode is screened by the interfacial fluid and induces subtle changes in its structure, which cannot be captured by the conventional Gouy-Chapman theory.

In recent years, this topic has been more intensively addressed in order to develop more efficient supercapacitors [2]. The latter are electrochemical devices that store the charge at the electrode/electrolyte interface through reversible ion adsorption. In order to understand the molecular mechanisms at play, we have performed molecular dynamics simulations on a variety of systems made of ionic liquids and electrodes of different geometries ranging from planar to nanoporous. A key aspect of our simulations is to use a realistic model for the electrodes, by allowing the local charges on the atoms to vary dynamically in response to the electrical potential caused by the ions and molecules in the electrolyte [3].

These simulations have allowed us to gain strong insight on the structure and dynamics of ionic liquids at electrified interfaces. From the comparison between graphite and nanoporous carbide-derived carbon (CDC) electrodes, we have elucidated the microscopic mechanism at the origin of the increase of the capacitance enhancement in nanoporous carbons [4]. We have also studied the impact of the carbon texture, by comparing CDC with perforated graphene materials [5].


1.       Fedorov, M.V., Kornyshev, A.A., Chem. Rev., 114 (2014), 2978-3036

2.       Salanne, M., Rotenberg, B., Naoi, K., Kaneko, K., Taberna, P.L., Grey, C.P., Dunn, B., Simon, P., Nature Energy, 1 (2016), 16070

3.       Merlet, C., Pean, C., Rotenberg, B., Madden, P.A., Simon, P., Salanne, M., J. Phys. Chem. Lett., 4 (2013), 264-268         

4.       Merlet, C., Rotenberg, B., Madden, P.A., Taberna, P.L., Simon, P., Gogotsi, Y., Salanne, M., Nature Materials, 11 (2012), 306-310

5.       Mendez-Morales, T., Burbano, M., Haefele, M., Rotenberg, B., Salanne M., J. Chem. Phys., 148 (2018), 193812

Mark Goerbig (LPS Orsay) et Bernard Plaçais (LPA ENS)

Surface states in topological materials beyond the chiral ones: from theory to experiment

We report on the anomalous screening by Dirac states in topological HgTe/CdHgTe heterojunctions in large transverse electric fields[1]. It is mesured in high frequency electronic compressibility experiments. Screening extends over a large chemical potentialrange of 300 meV widely exceeding the  30 meV bulk band gap of HgTe. Dirac screening breakdown is accompanied by an abrupt drop of the Dirac fermion mobility which we attribute to the existence of a series of massive interface states first introduced by Volkov and Pankratov (VP) [2]. Field-effect compressibility is a convenient scattering spectroscopy tool to investigate VP states. Their spectrum obbeys a Landau level energy series with a pseudo magnetic field determined by the Dirac fermon velocity and electric field [3].
[1] A. Inhofer et al., Phys. Rev. B 96, 195104 (2017).
[2] B.A. Volkov, O.A. Pankratov, JETP Lett. 42, 178 (1985).
[3] S. Tchoumakov et al., Phys. Rev. B 96, 201302-R (2017).

Martin Weigt (LCQB Jussieu)

Statistical-physics inspired modeling of protein sequences: Inferring structure, function, and mutational landscapes

Over the last years, biological research has been revolutionized by experimental high-throughput techniques. Unprecedented amounts of data are accumulating, causing an urgent need to develop data-driven modeling approaches to unveil information hidden in raw data, thereby helping to increase our understanding of complex biological systems. Inference approaches based on statistical physics have played an important role across diverse systems ranging from proteins over neural networks to the collective behaviour of animal groups. To give a specific example, proteins show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino-acid sequences. Thanks to modern sequencing techniques, this amino-acid variability is easily observable, contrary to time- and labour-intensive experiments determining, e.g., the three-dimensional fold of a protein or its biological functionality. I will present recent developments around the so-called Direct-Coupling Analysis, a statistical-mechanics inspired inference approach, which links sequence variability to protein structure and function. I will show that this methodology can be used to (i) to infer contacts between residues and thus to guide 3D-structure prediction of proteins and their complexes, (ii) to infer conserved protein-protein interaction networks, and (iii) to reconstruct mutational landscapes and thus to predict the effect of mutations. Beyond a direct biological and medical interest of such findings, they provide us also insight into underlying principles connecting protein evolution, structure and function.

Giulio Biroli (IPhT CEA Saclay et LPS-ENS)
Emergent phenomena in large interacting ecosystems

I will first start with a general introduction on theoretical ecology, stressing the 
reasons that make connections with statistical physics interesting and timely.  
I will then focus on Lotka-Volterra equations, which provide a general model to study large assemblies of strongly 
interacting degrees of freedom in many different fields: biology, economy and in particular ecology.  
I will present our analysis of Lotka-Volterra equations as model of ecosystems formed by a 
large number of species and show the different phases that emerge. Two of them are particularly 
interesting: when interactions are symmetric we find a regime characterised by an exponential 
number of multiple equilibria, all poised at the edge of stability for a large number of species.
For non symmetric interactions, this phase is replaced by a chaotic one.  
I will then conclude discussing relationships with experiments and general consequences of our works.