  In this work, we present a general machine learning approach for
full-dimensional potential energy surfaces for tetra-atomic systems. Our method
employs an active learning scheme trained on {\it ab initio} points, which size
grows based on the accuracy required. The training points are selected based on
molecular dynamics simulations, choosing the most suitable configurations for
different collision energy and mapping the most relevant part of the potential
energy landscape of the system. The present approach does not require
long-range information and is entirely general. As an example, we provide the
full-dimensional AlF-AlF potential energy surface, requiring $\lesssim 0.1\%$
of the configurations to be calculated {\it ab initio}. Furthermore, we analyze
the general properties of the AlF-AlF system, finding key difference with other
reported results on CaF or bi-alkali dimers.
