Category: Presentation

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Links:
Complex Lagrangian Problems of Particles in Flows program

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in microscopy, optical tweezers, and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy [1,2], to perform virtual staining of [3], to estimate the properties of anomalous diffusion [4,5,6], to characterize microscopic force fields [7], to improve the calculation of optical forces [8], and to characterize nanoparticles [9]. Finally, I will provide an outlook on the future for the application of deep learning in these fields.

References
[1] S. Helgadottir, A. Argun, and G. Volpe. Digital video microscopy enhanced by deep learning. Optica 6, 506 (2019).
[2] B. Midtvedt, S. Helgadottir, A. Argun, J. Pineda, D. Midtvedt, and G. Volpe. Quantitative digital microscopy with deep learning. Appl. Phys. Rev. 8, 011310 (2021).
[3] S. Helgadottir, B. Midtvedt, J. Pineda, et al. Extracting quantitative biological information from bright-field cell images using deep learning. Biophys. Rev. 2, 031401 (2021).
[4] S. Bo, F. Schmidt, R. Eichhorn, and G. Volpe. Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102 (2019).
[5] A. Argun, G. Volpe, and S. Bo. Classification, inference and segmentation of anomalous diffusion with recurrent neural networks. J. Phys. A: Math. Theor. 54, 294003 (2021).
[6] G. Muñoz-Gil, G. Volpe, M. A. Garcia-March, et al. Objective comparison of methods to decode anomalous diffusion. Nat. Commun. 12, 6253 (2021).
[7] A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe. Enhanced force-field calibration via machine learning. Appl. Phys. Rev. 7, 041404 (2020).
[8] I.C.D. Lenton, G. Volpe, A.B. Stilgoe, T.A. Nieminen, and H. Rubinsztein-Dunlop. Machine learning reveals complex behaviours in optically trapped particles. Mach. Learn.: Sci. Technol. 1, 045009 (2020).
[9] B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C.B. Adiels, G. Volpe, and D. Midtvedt. Fast and accurate nanoparticle characterization using deep-learning-enhanced off-axis holography. ACS Nano 15, 2240 (2021).

Link: Physics Days 2022 – Future Leaders
The Physics Days 2022 is organized by the Finnish Physical Society and the Department of Applied Physics at Aalto University.

Critical Casimir forces (CCF) are a powerful tool to control the self-assembly and complex behavior of microscopic and nanoscopic colloids. While CCF were theoretically predicted in 1978 [1], their first direct experimental evidence was provided only in 2008, using total internal reflection microscopy (TIRM) [2]. Since then, these forces have been investigated under various conditions, for example, by varying the properties of the involved surfaces or with moving boundaries. In addition, a number of studies of the phase behavior of colloidal dispersions in a critical mixture indicate critical Casimir forces as candidates for tuning the self-assembly of nanostructures and quantum dots, while analogous fluctuation-induced effects have been investigated, for example, at the percolation transition of a chemical sol, in the presence of temperature gradients, and even in granular fluids and active matter. In this presentation, I’ll give an overview of this field with a focus on recent results on the measurement of many-body forces in critical Casimir forces [3], the realization of micro- and nanoscopic engines powered by critical fluctuations [4, 5], and the creation of light-controllable colloidal molecules [6] and active droploids [7].

References

[1] ME Fisher and PG de Gennes. Phenomena at the walls in a critical binary mixture. C. R. Acad. Sci. Paris B 287, 207 (1978).
[2] C Hertlein, L Helden, A Gambassi, S Dietrich and C Bechinger. Direct measurement of critical Casimir forces. Nature 451, 172 (2008).
[3] S Paladugu, A Callegari, Y Tuna, L Barth, S Dietrich, A Gambassi and G Volpe. Nonadditivity of critical Casimir forces. Nat. Commun. 7, 11403 (2016).
[4] F Schmidt, A Magazzù, A Callegari, L Biancofiore, F Cichos and G Volpe. Microscopic engine powered by critical demixing. Phys. Rev. Lett. 120, 068004 (2018).
[5] F Schmidt, H Šípová-Jungová, M Käll, A Würger and G Volpe. Non-equilibrium properties of an active nanoparticle in a harmonic potential. Nat. Commun. 12, 1902 (2021).
[6] F Schmidt, B Liebchen, H Löwen and G Volpe. Light-controlled assembly of active colloidal molecules. J. Chem. Phys. 150, 094905 (2019).
[7] J Grauer, F Schmidt, J Pineda, B Midtvedt, H Löwen, G Volpe and B Liebchen. Active droploids. Nat. Commun. 12, 6005 (2021).

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in experimental soft matter.