  The influence of microscopic force fields on the motion of Brownian particles
plays a fundamental role in a broad range of fields, including soft matter,
biophysics, and active matter. Often, the experimental calibration of these
force fields relies on the analysis of the trajectories of these Brownian
particles. However, such an analysis is not always straightforward, especially
if the underlying force fields are non-conservative or time-varying, driving
the system out of thermodynamic equilibrium. Here, we introduce a toolbox to
calibrate microscopic force fields by analyzing the trajectories of a Brownian
particle using machine learning, namely recurrent neural networks. We
demonstrate that this machine-learning approach outperforms standard methods
when characterizing the force fields generated by harmonic potentials if the
available data are limited. More importantly, it provides a tool to calibrate
force fields in situations for which there are no standard methods, such as
non-conservative and time-varying force fields. In order to make this method
readily available for other users, we provide a Python software package named
DeepCalib, which can be easily personalized and optimized for specific
applications.
