Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model

Modern microscopy techniques can be used to investigate
soft nano-objects
at the nanometer scale. However, time-consuming microscopy measurements
combined with low numbers of observable polydisperse objects often
limit the statistics. We propose a method for identifying the most
representative objects from their respective point clouds. These point
cloud data are obtained, for example, through the localization of
single emitters in super-resolution fluorescence microscopy. External
stimuli, such as temperature, can cause changes in the shape and properties
of adaptive objects. Due to the demanding and time-consuming nature
of super-resolution microscopy experiments, only a limited number
of temperature steps can be performed. Therefore, we propose a deep
generative model that learns the underlying point distribution of
temperature-dependent microgels, enabling the reliable generation
of unlimited samples with an arbitrary number of localizations. Our
method greatly cuts down the data collection effort across diverse
experimental conditions, proving invaluable for soft condensed matter
studies.