Data from: Multioutput convolutional neural network for improved parameter extraction in time-resolved electrostatic force microscopy data

Time-resolved scanning probe microscopy methods, like time-resolved
electrostatic force microscopy (trEFM), enable imaging of dynamic
processes ranging from ion motion in batteries to electronic dynamics in
microstructured thin film semiconductors for solar cells. Reconstructing
the underlying physical dynamics from these techniques can be challenging
due to the interplay of cantilever physics with the actual transient
kinetics of interest in the resulting signal. Previously, quantitative
trEFM used empirical calibration of the cantilever or feed-forward neural
networks trained on simulated data to extract the physical dynamics of
interest. Both these approaches are limited by interpreting the underlying
signal as a single exponential function, which serves as an approximation
but does not adequately reflect many realistic systems. Here, we present a
multi-branched, multi-output convolutional neural network (CNN) that uses
the trEFM signal in addition to the physical cantilever parameters as
input. The trained CNN accurately extracts parameters describing both
single-exponential and bi-exponential underlying functions, and more
accurately reconstructs real experimental data in the presence of noise.
This work demonstrates an application of physics-informed machine learning
to complex signal processing tasks, enabling more efficient and accurate
analysis of trEFM.