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We develop a high-dimensional neural network potential (NNP) to describe thestructural and energetic properties of borophene deposited on silver. This NNPhas the accuracy of DFT calculations while achieving computational speedups ofseveral orders of magnitude, allowing the study of extensive structures thatmay reveal intriguing moir\'e patterns or surface corrugations. We describe anefficient approach to constructing the training data set using an iterativetechnique known as the "adaptive learning approach". The developed NNPpotential is able to produce, with an excellent agreement, the structure,energy and forces of DFT. Finally, the calculated stability of variousborophene polymorphs, including those not initially included in the trainingdataset, shows better stabilization for $\nu\sim0.1$ hole density, and inparticular for the allotrope $\alpha$ ($\nu=\frac{1}{9}$). The stability ofborophene on the metal surface is shown to depend on its orientation, implyingstructural corrugation patterns that can only be observed from long timesimulations on extended systems. The NNP also demonstrates its ability tosimulate vibrational densities of states and produce realistic structures, withsimulated STM images closely matching the experimental ones.

Neural network approach for a rapid prediction of metal-supported borophene properties