Formation of 2D silicon-carbide nanoribbons by cooling from the melt and out-of-plane displacements of atoms

Van Hoang, Vo; Le Nguyen, Tue Minh; Nguyen, Hang T. T.

doi:10.1007/s11051-023-05883-3

Cited by 1 publication

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capability of the MLIP to model this peritectic reaction, and subsequent graphitic structure formation, is seen as an advancement of the current state of the art. In contrast, the Vashishta potential does not capture this peritectic reaction, instead predicting a homogeneous melt process forming a stoichiometric liquid at 2768 K without graphitic structure formation 101,102 (in disagreement with experimental phase diagrams 103 ). Similarly, the Tersoff potential, 104 requires the manual removal of a silicon atom to simulate this peritectic reaction.…”

Section: Respectively)mentioning

confidence: 79%

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

MacIsaac,

Bavdekar,

Spearot

et al. 2024

J. Phys. Chem. C

View full text Add to dashboard Cite

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.

show abstract