  Prediction of pair potential given a typical configuration of an interacting
classical system is a difficult inverse problem. There exists no exact result
that can predict the potential given the structural information. We demonstrate
that using machine learning (ML) one can get a quick but accurate answer to the
question: which pair potential lead to the given structure (represented by pair
correlation function)? We use artificial neural network (NN) to address this
question and show that this ML technique is capable of providing very accurate
prediction of pair potential irrespective of whether the system is in a
crystalline, liquid or gas phase. We show that the trained network works well
for sample system configurations taken from both equilibrium and out of
equilibrium simulations (active matter systems) when the later is mapped to an
effective equilibrium system with a modified potential. We show that the ML
prediction about the effective interaction for the active system is not only
useful to make prediction about the MIPS (motility induced phase separation)
phase but also identifies the transition towards this state.
