Melting and Phase Separation of SiC from Large-scale Machine Learning Molecular Dynamics

Incongruent melting has been reported by many experimental studies with the observation that the SiC decomposes into liquid silicon and solid carbon upon heating. However, contradictory results have also been reported that SiC melts congruently with no decomposition. Ab initio MD provides evidence of carbon clustering during the melting process but did not capture the phase separation limited by the small supercell. Empirical potentials give good approximations of the melting points but all of them have limited ability to describe other phases and thus cannot capture the decomposition in the MD simulation. In this work, we use Bayesian active learning to train a machine learning potential for SiC at a wide range of temperatures and pressures, and observe the phase decomposition process into Si and C-rich phases in both the active learning and the large-scale MD stages. Our results provide a microscopic validation of the incongruent melting hypothesis, resolving the disagreement among experiments of the SiC melting behavior. The occurrence of decomposition also indicates that amorphous SiC can not be produced by melting and quenching but only with irradiation, consistent with existing experimental approaches of the amorphization of SiC.