  Silicon is an important material and many empirical interatomic potentials
have been developed for atomistic simulations of it. Among them, the Tersoff
potential and its variants are the most popular ones. However, all the existing
Tersoff-like potentials fail to reproduce the experimentally measured thermal
conductivity of diamond silicon. Here we propose a modified Tersoff potential
and develop an efficient open source code called GPUGA (graphics processing
units genetic algorithm) based on the genetic algorithm and use it to fit the
potential parameters against energy, virial and force data from quantum density
functional theory calculations. This potential, which is implemented in the
efficient open source GPUMD (graphics processing units molecular dynamics)
code, gives significantly improved descriptions of the thermal conductivity and
phonon dispersion of diamond silicon as compared to previous Tersoff potentials
and at the same time well reproduces the elastic constants. Furthermore, we
find that quantum effects on the thermal conductivity of diamond silicon at
room temperature are non-negligible but small: using classical statistics
underestimates the thermal conductivity by about 10\% as compared to using
quantum statistics.
