Optimization of self-interstitial clusters in 3C-SiC with Genetic Algorithm

TL;DR

This study uses a genetic algorithm to identify stable self-interstitial atom cluster structures in 3C-SiC, revealing how cluster composition and shape evolve with size, which is crucial for understanding radiation damage.

Contribution

It applies a genetic algorithm to explore and determine the ground-state structures of defect clusters in 3C-SiC, incorporating both DFT and empirical potentials for the first time.

Findings

C-only clusters dominate for small sizes (n <= 10)

Clusters tend to be stoichiometric for larger sizes (n > 10)

Small clusters are spherical, larger clusters are planar

Abstract

Under irradiation, SiC develops damage commonly referred to as black spot defects, which are speculated to be self-interstitial atom clusters. To understand the evolution of these defect clusters and their impacts (e.g., through radiation induced swelling) on the performance of SiC in nuclear applications, it is important to identify the cluster composition, structure, and shape. In this work the genetic algorithm code StructOpt was utilized to identify groundstate cluster structures in 3C-SiC. The genetic algorithm was used to explore clusters of up to ~30 interstitials of C-only, Si-only, and Si-C mixtures embedded in the SiC lattice. We performed the structure search using Hamiltonians from both density functional theory and empirical potentials. The thermodynamic stability of clusters was investigated in terms of their composition (with a focus on Si-only, C-only, and stoichiometric) and shape (spherical vs. planar), as a function of the cluster size (n). Our results suggest that large Si-only clusters are likely unstable, and clusters are predominantly C-only for n <= 10 and stoichiometric for n > 10. The results imply that there is an evolution of the shape of the most stable clusters, where small clusters are stable in more spherical geometries while larger clusters are stable in more planar configurations. We also provide an estimated energy vs. size relationship, E(n), for use in future analysis.