“…For nuclear physics, ML applications can be traced back to early 1990s [26,27], and recently, it has been widely adopted to nuclear masses [28][29][30][31][32][33][34][35][36][37][38][39][40][41], charge radii [36,[42][43][44][45], decays and reactions [46][47][48][49][50][51][52][53], ground and excited states [54][55][56][57][58], nuclear landscape [59,60], fission yields [61-63], nuclear liquid-gas phase transition [64], variational calculations [65,66], nuclear energy density functional [67], etc. In nuclear mass studies, ML approaches, such as the radial basis function (RBF) approach [28,29,[68][69][70][71], the Bayesian neural network (BNN) approach [31][32][33]72], and the kernel ridge regression (KRR) appro...…”