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A methodology is presented for developing transferable empirical potential functions without following the usual procedure of postulating a functional form. Instead, a neural network (NN) is employed to learn the functional relationships of potential energy surfaces from the local geometric arrangement of atoms. The methodology is illustrated by training the NN model on tens of thousands of individual...