Structure
and diffusion dynamics of silicon carbide (Si1–x
C
x
) are investigated
via molecular dynamics computer simulations with ab initio-based neural
network potentials, exploring the effect of composition and temperature
on crystallization behaviors. A neural network potential is developed
to describe high-dimensional potential energy surfaces of silicon
carbide (SiC) systems, reproducing first-principles results on their
potential energies and forces. The phase behavior of amorphous Si1–x
C
x
below
its experimental melting point is systematically demonstrated by analyzing
the structural and dynamic properties as a function of temperature
and carbon concentration x in the composition range
0 ≤ x ≤ 0.5 and the temperature range T = 2000–2600 K, compared to available experiments.
The phase of Si1–x
C
x
is characterized by analyzing the pair correlation
function, coordination number, tetrahedral order parameter, SiC tetrahedron
fraction, Si disordered fraction, and excess entropy. Our results
indicate that the system undergoes the crystallization by organizing
the short- and medium-range order as the carbon content increases,
where the critical carbon fraction for crystallization increases with
temperature. The addition of carbon to silicon results in the phase
separation into liquid Si and crystal SiC as well as the partial crystallization
of Si1–x
C
x
. The self-diffusivity of Si1–x
C
x
is also evaluated to understand
how the structural change caused by the crystallization works on diffusion
dynamics. The diffusion dynamics of Si1–x
C
x
becomes slower with increasing
carbon content and decreasing temperature, which significantly slows
down with onset of the crystallization.