The fourth round table of the theoretical training took place with the participation of our panelists: Hartmut Löwen, Joakim Stenhammar, Holger Stark and Ramin Golestanian. The organizing ESRs were Ayten Gülce Bayram, Chun-Jen Chen, Jérémie Bertrand, Jesus Manuel Antunez Dominguez, Ojus Satish Bagal, Alireza Khoshzaban and Umar Rauf. The discussion mainly addressed to “Theoretical Models for Active matter”.
The discussion started with how the activity is included in theoretical models and how activity terms change depending on the particles system. It is followed by the theoretical aspects of going from one particle to the many-particle system and the relevant interaction terms in the theoretical models. Next, we mentioned the challenges behind the solvent-particle interaction, especially where we have complex solvents like a viscoelastic solvent. In this regard, our guests pointed out the importance of hydrodynamics. The meeting was concluded with the final remarks of our guests on the discussion that we should keep in mind in our future studies on active matter physics.
Today the second round table of the Initial Training on Theoretical Methods took place, entitled “Theoretical aspects of collective behavior”. The round table was hosted by ESRs David, Jesus, Ojus, Carolina, Alireza, Dana, and Umar. The inspiring group of speakers included Margarida Telo da Gama, Fernando Peruani, Nicoletta Gnan, and Claudio Maggi.
Many matters were discussed, ranging from the limits of collective behavior and the role of communication in emergence, to the compatibility between experiments and theory of collective behavior. Examples can be found in both natural and artificial environments, even combinations with varying degrees of active motion. This adds to the challenge of defining valuable, even if not accurate, models. At the core, collective behavior highlights how the system can be much more than just the sum of individual entities.
With a joint effort of the ESR students, a new logo for the ActiveMatter website was designed. The idea started as a handdrawing on a piece of paper and was quickly adapted to a better version with drawing softwares. More than 15 logos were suggested and submitted to a vote. The competition was fierce but we all came to agree on one of them and we are happy to present you the new official logo of the ITN ActiveMatter !
The last round table of this workshop regarded the topic advanced control of active matter. As organizers of round table, Audrey Nsamela, Chun-Jen Chen, Sandrine Heijnen, Harshith Bachimanchi and Alireza Khoshzaban, we welcomed and introduced our esteemed guests, namely Jérémie Palacci from University of San Diego, Clemens Bechinger from Konstanz University, Frank Cichos from Leipzig University, and Lucio Isa from ETH Zurich.
The round table started out with a clarification on advanced
control of active matter. Active matter can be controlled by numerous external stimuli but implementing control on individual particles or artificial entities is what qualifies as advanced control. Currently, the control of active matter is still far from the behavior and control micro-organisms have on that scale; hence
a big challenge lies there for us. Jérémie Palacci introduced an interesting research topic where they found a way to regulate the swimming process of E. Coli by light illumination. Here genetic modification was used to control the proton pump involved in the energy transportation process.
Advanced control of active matter can be applied to model
systems where the control is lacking, for example biological systems. In a biological system the control over an organism is limited to the external stimuli that are applied and won’t always result in the same reaction.
Therefore, using active particles showing predictable and reproducible behaviors when exposed to a stimulus works perfectly to model and to probe different parameters and thus provide a deeper understanding of the system. The fact advanced control of active matter doesn’t have an application outside of modelling
systems is something we shouldn’t be ashamed of.
We concluded the meeting by asking every one of our guests what
the promising research directions in the advanced control of active matter are.
All of them had a different perspective. Starting with Clemens Bechinger, who was most invested in the further exploration of the applications for model systems. Lucio Isa is mainly looking forward to explore the different materials that we can use to create active material that can subsequently be controlled. Frank Cichos
mentioned the importance of looking into new ways to create active particles. So far nature was able to achieve production of active entities with limited waste whereas human production is rather inefficient. Jérémie Palacci pointed out that the current man-made active matter systems are reacting to a strong signal in a well-controlled environment, where nature faces many more factors and
still works. It would be interesting to design a system that is resistant to noise.
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.