Calibration of force fields using recurrent neural networks

The calibration of physical force fields from particle trajectories is important for experiments in soft matter, biophysics, active matter, and colloidal science. However, it is not always possible to have a standard method to characterize a force field, especially for systems that are out of equilibrium. Here, we introduce a generic toolbox for calibrating any kind of conservative or non-conservative, fixed or time-varying potentials that is powered by recurrent neural networks (RNN). We show that with the help of neural networks, we can outperform standard methods as well as analyze systems that cannot be approached by existing methods. We provide a software package that is available online for free access.