  Reactive molecular dynamics (MD) simulation is performed using a reactive
force field (ReaxFF). To this end, we developed a new method to optimize the
ReaxFF parameters based on a machine learning approach. This approach combines
the $k$-nearest neighbor and random forest regressor algorithm to efficiently
locate several possible ReaxFF parameter sets, thereby the optimized ReaxFF
parameter can predict physical properties even in a high-temperature condition
within a small effort of parameter refinement. As a pilot test of the developed
approach, the optimized ReaxFF parameter set was applied to perform chemical
vapor deposition (CVD) of an $\alpha$-Al$_2$O$_3$ crystal. The crystal
structure of $\alpha$-Al$_2$O$_3$ was reasonably reproduced even at a
relatively high temperature (2000 K). The reactive MD simulation suggests that
the (11$\overline{2}$0) surface grows faster than the (0001) surface,
indicating that the developed parameter optimization technique could be used
for understanding the chemical reaction in the CVD process.
