Validation workflow for machine learning interatomic potentials for complex ceramics

Ghaffari, Kimia; Bavdekar, Salil; Spearot, Douglas E.; Subhash, Ghatu

doi:10.1016/j.commatsci.2024.112983

Cited by 1 publication

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, MLIPs have been developed predominately for single element metallic materials, such as tungsten, , iron, , aluminum, , etc. While a few models for ceramics have been developed, − they are not nearly as prevalent. Ceramics possess attributes such as high hardness, chemical inertness, low density, high temperature resistance, oxidation and corrosion resistance, etc., that are well suited for use in harsh environments.…”

Section: Introductionmentioning

confidence: 99%

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

MacIsaac,

Bavdekar,

Spearot

et al. 2024

J. Phys. Chem. C

Self Cite

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