Aykut Argun (Soft Matter Lab) and Stefano Bo (MPI Dresden) participated in the AnDi Challenge, the Anomalous Diffusion challenge, in all the nine categories.
The challenge consisted of different tasks, specifically:
Task 1 – Inference of the anomalous diffusion exponent α.
Task 2 – Classification of the diffusion model.
Task 3 – Segmentation of trajectories.
Each task included modalities for different number of dimensions (1D, 2D and 3D), for a total of 9 subtasks.
Approximately 20 teams from all the world participated in the challenge.
Aykut’s and Stefano’s team, eduN, ranked in the first three positions in all the categories. EduN won the 1st place in 4 of the categories, i.e., Task 2 (1D and 2D), and Task 3 (1D and 3D), the 2nd place in another 4 categories, and 3rd in the remaining category.
The details and the information about the final results can be found on ANDI Challenge final results page: http://www.andi-challenge.org/ (select: Learn the Details and then Final Results)
Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
Applied Physics Reviews 7, 041404 (2020)
The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.
Measurement of Anomalous Diffusion Using Recurrent Neural Networks
Stefano Bo, Falko Schmidt, Ralf Eichborn & Giovanni Volpe
Physical Review E 100(1), 010102(R) (2019)
Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement (MSD) with time has an exponent different from one. We show that recurrent neural networks (RNN) can efficiently characterize anomalous diffusion by determining the exponent from a single short trajectory, outperforming the standard estimation based on the MSD when the available data points are limited, as is often the case in experiments. Furthermore, the RNN can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and estimating the switching time and anomalous diffusion exponents of an intermittent system that switches between different kinds of anomalous diffusion. We validate our method on experimental data obtained from sub-diffusive colloids trapped in speckle light fields and super-diffusive microswimmers.
Experimental realization of a minimal microscopic heat engine
Aykut Argun, Jalpa Soni, Lennart Dabelow, Stefano Bo, Giuseppe Pesce, Ralf Eichhorn & Giovanni Volpe
Physical Review E 96(5), 052106 (2017)
Microscopic heat engines are microscale systems that convert energy flows between heat reservoirs into work or systematic motion. We have experimentally realized a minimal microscopic heat engine. It consists of a colloidal Brownian particle optically trapped in an elliptical potential well and simultaneously coupled to two heat baths at different temperatures acting along perpendicular directions. For a generic arrangement of the principal directions of the baths and the potential, the symmetry of the system is broken, such that the heat flow drives a systematic gyrating motion of the particle around the potential minimum. Using the experimentally measured trajectories, we quantify the gyrating motion of the particle, the resulting torque that it exerts on the potential, and the associated heat flow between the heat baths. We find excellent agreement between the experimental results and the theoretical predictions.