A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon–Carbon System
Michael MacIsaac,
Salil Bavdekar,
Douglas Spearot
et al.
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… Show more
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