Quantitative comparison of a microgravity dusty plasma to a liquid crystal using sparse regression

Demonstrating the universality of phase transitions and understanding the origins of pattern formation and defect propagation in liquid crystals can enable various applications in soft robotics, artificial muscles, LC polymer science, and nano-technology. Here we propose that microgravity dusty plasmas can be used as analogue systems for the investigation of universal properties of the LC structural states. A dusty (complex) plasma consists of neutral particles, ions, electrons, and micro- or nano-particles which charge upon entering the system and become coupled to the background plasma. Dusty plasma experiments at the Plasmakristall-4 (PK-4) facility on board the International Space Station allow for constructing 3D dust clouds where the dust-dust interaction potential can vary under external electric field, resulting in the formation of LC-like structures. Dusty plasmas are useful analogue systems as the particles are directly imaged, thus allowing for a reconstruction of the entire phase space. To demonstrate the validity of a dusty plasma as a macroscopic analogue for the study of LC structural transitions, we propose to use eXtended Lagrangian Sparse Identification of Nonlinear Dynamics (xL-SINDy) [1] to extract information about the variation of the dust interaction potential from video data.

[1] Purnomo, A. and Hayashibe, M. (2023). “Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method.” Sci. Reports 19 7919. https://doi.org/10.1038/s41598-023-34931-0