  A linear regression-based machine learned interatomic potential (MLIP) was
developed for the silicon-carbon system. The MLIP was predominantly trained on
structures discovered through a genetic algorithm, encompassing the entire
silicon-carbon composition space, and uses as its foundation the Ultra-Fast
Force Fields (UF3) formulation. To improve MLIP performance, the learning
algorithm was modified to include higher spline interpolation resolution in
regions with large potential energy surface curvature. The developed MLIP
demonstrates exceptional predictive performance, accurately estimating energies
and forces for structures across the silicon-carbon composition and
configuration space. The MLIP predicts mechanical properties of SiC with high
precision and captures fundamental volume-pressure and volume-temperature
relationships. Uniquely, this silicon-carbon MLIP is adept at modeling complex
high-temperature phenomena, including the peritectic decomposition of SiC and
carbon dimer formation during SiC surface reconstruction, which cannot be
captured with prior classical interatomic potentials for this material.
