  The neural-network interatomic potential for crystalline and liquid Si has
been developed using the forward stepwise regression technique to reduce the
number of bases with keeping the accuracy of the potential. This approach of
making the neural-network potential enables us to construct the accurate
interatomic potentials with less and important bases selected systematically
and less heuristically. The evaluation of bulk crystalline properties, and
dynamic properties of liquid Si show good agreements between the neural-network
potential and ab-initio results.
