The fourth roundtable was an opportunity for all students to discuss the topic “Collective Behavior” on Zoom with a panel of guests: Clemens Bechinger from the University of Konstanz, Ivo Buttinoni from Heinrich Heine University in Dusseldorf and Caroline Beck Adiels from Gothenburg University. The event was organized by Daniela Pérez, Danne van Roon, Davide Breoni, Jérémie Bertrand, Laura Natali and Liam Ruske on March 24th.
Although the guests had different background they seemed to agree on the fact that complex behavior can emerge from an ensemble of entities that obey a small number of simple rules. Indeed, minimalistic models such as the Vicsek model account for phase transition from a disordered motion to large scale motion and more; phenomena that appear to be universal.
A question on the role of intelligence and communication in collective behavior started the discussion. Although some animals or colony of bacteria may seem intelligent (e.g. escaping from a predator in a clever way or making long-lasting symbiotic microfilms), we must bear in mind that collective behavior is… collective, and rarely arises from decisions made individually. It may be said that in the animal kingdom, the need for survival requires a need to adapt and therefore to be intelligent, but this need for intelligence can be outsourced and solved at the level of the group rather than hardwired in the physical brain of each animal (or human).
It is also conceivable that one of the entities acts as a leader and ignites a collective behavior. Giovanni Volpe made an interesting remark, stating that a leader is the one who defines the objective function to be optimized by the group. The idea of leadership in collective behavior of microscopic systems remain largely unexplored by physicists.
After one hour of fruitful discussion and back and forth between the students and the guests, the session was finished and we resumed our activities with a better understanding of collective behavior. We thank the panelists for their inputs and attendance!
The third round table session of the experimental training was about machine learning and its role in science, in particular physics and active matter. The panelists invited to the discussion were Carlo Manzo from Vic University, Benjamin Midtvedt and Saga Helgadottir from Gothenburg University, Onofrio Maragò and Alessandro Magazzù from CNR ICPF-Messina. The discussion was organized and lead by Jesus M. A. Dominguez, Davide Breoni, Liam Ruske, Chun-Jen Chen and Alireza Khoshzaban, who are students attending the training.
The discussion touched topics like the applications of machine learning in fields like optics, biophysics, medical research, the potentialities and the reliability of the method. Questions on when a machine learning approach is advisable and how cautious one must be when applying machine learning were also addressed. Current important logical and practical aspects of the method were also discussed, together with the need of testing machine learning applications against more classical ones. The panelists also stated the importance of reliably checking the results obtained to avoid biases that can lead to false conclusions.
After one hour of fruitful discussion we gained a broader perspective and a deeper understanding of machine learning.
Active Brownian and inertial particles in disordered environments: short-time expansion of the mean-square displacement
Davide Breoni, Michael Schmiedeberg, Hartmut Löwen
We consider an active Brownian particle moving in a disordered two-dimensional energy or motility landscape. The averaged mean-square-displacement (MSD) of the particle is calculated analytically within a systematic short-time expansion. As a result, for overdamped particles, both an external random force field and disorder in the self-propulsion speed induce ballistic behaviour adding to the ballistic regime of an active particle with sharp self-propulsion speed. Spatial correlations in the force and motility landscape contribute only to the cubic and higher order powers in time for the MSD. Finally, for inertial particles two superballistic regimes are found where the scaling exponent of the MSD with time is α = 3 and α = 4. We confirm our theoretical predictions by computer simulations. Moreover they are verifiable in experiments on self-propelled colloids in random environments.
The ActiveMatter PI+ESRs meeting took place on 10 September 2020. Because of the current travel restrictions and regulations imposed to hinder the spread of the CoViD-19 epidemics, the meeting was held online.
The aim of the meeting was to give an update to all the members on the progress of the ActiveMatter network.
Currently 12 of the 15 Early Stage Researchers (ESRs) have already been recruited and could started their project. During the meeting the ESRs had the opportunity to introduce themselves to the rest of the network and to present their research project.