Applications of MLPs are often limited by the generalizability thereof, and the difficulty of controlling it, especially when the simulation timescale goes beyond what is possible for the reference calculations. An adaptive learn-onthe-fly (AcLe) algorithm has been developed to address this challenge, along with workflows PiNNAcLe that interface PiNN with different packages that perform electronic structure calculations, such as CP2K. In a nutshell, the AcLe algorithm performs the validation of an MLP at an adaptive timescale, which eliminates the need of uncertainty quantification methods, and the associated computational cost and overconfidence often incurred by such methods. This makes PiNNA-cLe particularly useful for predicting properties that require robust sampling at a much longer timescale, such as the computation of transport coefficients in concentrated electrolyte solutions using linear response theory. Details of the AcLe algorithm and the corresponding code PiNNAcLe are described and distributed elsewhere.