A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon–Carbon System

Abstract

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 (UF 3 ) 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 structural, energetic, and elastic properties of silicon carbide (SiC) with high precision and captures fundamental volume-pressure and volumetemperature 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.