Invited talk by G. Volpe at BRC Day “Biomaterials meets AI”, Gothenburg, Sweden, 12 November 2019

Machine learning as a tool for the natural sciences: Opportunities and challenges
Giovanni Volpe
Invited Talk at BRC Day “Biomaterials meets AI”, University of Gothenburg, Gothenburg, Sweden, 12 November 2019

Abstract: Data-driven machine-learning methods are more and more widely used in the natural sciences. Machine learning offers unprecedented opportunities, but it also poses unexpected practical and fundamental challenges. Most importantly, machine-learning methods often work as black boxes, and therefore it can be difficult to understand and interpret their results. Here, we present an overview of the current state of the art of the adoption of machine learning in active-matter research. Finally, we discuss the opportunities and challenges that are emerging, highlighting how active matter and machine learning can work together for mutual benefit.

Bio: Giovanni Volpe is Associate Professor at the University of Gothenburg, where he leads the Soft Matter Lab (http://www.softmatterlab.org/).
He has published more than 80 articles on diverse topics including optical trapping, active matter, neurosciences, and machine learning.
He has co-authored the book “Optical Tweezers: Principles and Applications” (Cambridge University Press, 2015).
He is the recipient the ERC Starting Grant ComplexSwimmers, coordinator of the MSCA Innovative Training Networks ActiveMatter, and the KAW research grant “Active Matter Goes Smart”.
He is one of the chairs of the Conference Emerging Topics in Artificial Intelligence at the SPIE Optics & Photonics Meeting held annually in San Diego (CA).

Active Colloids with Position-Dependent Rotational Diffusivity on ArXiv

Active Colloids with Position-Dependent Rotational Diffusivity

Active Colloids with Position-Dependent Rotational Diffusivity
Miguel Angel Fernandez-Rodriguez, Fabio Grillo, Laura Alvarez, Marco Rathlef, Ivo Buttinoni, Giovanni Volpe & Lucio Isa
arXiv: 1911.02291

The non-thermal nature of self-propelling colloids offers new insights into non-equilibrium physics. The central mathematical model to describe their trajectories is active Brownian motion, where a particle moves with a constant speed, while randomly changing direction due to rotational diffu- sion. While several feedback strategies exist to achieve position-dependent velocity, the possibility of spatial and temporal control over rotational diffusion, which is inherently dictated by thermal fluctuations, remains untapped. Here, we decouple rotational diffusion from thermal noise. Using external magnetic fields and discrete-time feedback loops, we tune the rotational diffusivity of active colloids above and below its thermal value at will and explore a rich range of phenomena including anomalous diffusion, directed transport, and localization. These findings add a new dimension to the control of active matter, with implications for a broad range of disciplines, from optimal transport to smart materials.