Nuclear mass predictions based on a deep neural network and finite-range droplet model (2012)* *H.L. acknowledges the JSPS Grant-in-Aid for Early-Career Scientists under Grant No. 18K13549, the JSPS Grant-in-Aid for Scientific Research (S) under Grant No. 20H05648, and the RIKEN Pioneering Project: Evolution of Matter in the Universe. J.L. acknowledges the Research Grants Council (RGC) of Hong Kong, China with

Abstract

A neural network with two hidden layers is developed for nuclear mass prediction, based on the finite-range droplet model (FRDM12). Different hyperparameters, including the number of hidden units, choice of activation functions, initializers, and learning rates, are adjusted explicitly and systematically. The resulting mass predictions are achieved by averaging the predictions given by several different sets of hyperparameters with different regularizers and seed numbers. This can provide not only the average values of mass predictions but also reliable estimations in the mass prediction uncertainties. The overall root-mean-square deviations of nuclear mass are reduced from 0.603 MeV for the FRDM12 model to 0.200 MeV and 0.232 MeV for the training and validation sets, respectively.