  The region around the doubly magic nuclide $^{100}Sn$ is very interesting for
nuclear physics studies in terms of structure, reaction and nuclear
astrophysics. The main ingredients in nuclear structure studies using the shell
model are the single-particle energies and the two-body matrix elements. To
obtain the former, experimental data of $^{101}Sn$ isotope spectrum are
necessary. Since there is not enough experimental data, different approaches
are used in the literature to obtain single-particle energies. In sn100pn
interaction, the hole excitation spectrum was used in $^{131}Sn$ to determine
neutron single-particle energies. The other approach is the use of the lightest
isotope, $^{107}Sn$, which figures the model space orbitals. In this study, we
estimated the spectrum of the $^{101}Sn$ isotope by artificial neural network
method in order to obtain neutron single-particle energies. After the training
was carried out by using the experimental spectra of the nuclei around
$^{100}Sn$ isotope, the $^{101}Sn$ spectrum was obtained. Subsequently, neutron
SPEs of the model space orbitals are defined. Shell model calculations for
$^{102-108}Sn$ isotopes are carried out and results are compared to the
experimental data and results obtained using the widely used interaction in the
region, sn100pn. According to the results, it is seen that the Sn isotope
spectra obtained with the new SPE values are more compatible with the
experimental data.
