  We examine a new method for predicting the atomization energies of Au13+
clusters by a nonlinear regression model using interatomic and centroid
distances as descriptors to improve the efficiency of density-functional theory
calculations. Learning data were created using the time-series data of atomic
coordinates and Kohn-Sham energy generated by molecular-dynamics simulations.
This approach predicted the atomization energies of fifteen known
stable/metastable structures of Au13+ clusters well. Moreover, we found that
the fitting to the test data could be markedly improved by eliminating the
descriptors representing the short interatomic distance.
