April 11, 2022
(Left) Gardel realized an active liquid crystal where the active stresses could be modulated spatiotemporally through selective illumination with blue light. In the image, the region of high activity is enclosed within the red rectangle (Right). dePablo, Dinner, and Vitelli have constructed both hydrodynamic simulations and machine learning algorithms that faithfully predict the dynamics of the liquid crystal with structured activity (Right bottom). The structured activity is used to guide the motion of defects (indicated by the yellow arrowhead) and boundaries of high and low activity (indicated by the red shading). Defects are constrained to regions of high activity.
Zhang R, et al., Spatiotemporal control of liquid crystal structure and dynamics through activity patterning.
Nature Materials. 2021.
Colen J, et al., Machine learning active-nematic hydrodynamics. Proc Natl Acad Sci USA. 2021.