From Migration–Healing Reconstruction to Chemical Segregation: Atomistic Origins of High-Temperature Evolution in Cubic Boron Nitride Nanoparticles Revealed by Machine Learning Potential Simulations

Abstract

Cubic boron nitride (c-BN) nanoparticles are promising for extreme-condition applications, yet their atomistic evolution remains poorly understood. Here, we develop a high-fidelity machine learning potential and perform large-scale deep potential molecular dynamics simulations to investigate their high-temperature behavior. A universal reconstruction pathway is revealed, involving defect formation, inward-to-outward atomic migration, and progressive healing into multilayer hexagonal BN (h-BN). This mechanism is validated across multiple morphologies and exposed facets and is found to be strongly dependent on facet and termination. Furthermore, temperature-programmed dynamics identify ∼1800 K as the critical threshold for activating large-scale atomic flux, driving the transformation from core–shell architectures to multishell fullerene-like h-BN structures. At extreme temperatures (>3300 K), chemical segregation emerges, leading to the formation of boron clusters and polynitrogen chains, consistent with experimental observations. We further compared the reconstruction behaviors of isoelectronic nanodiamond and c-BN nanoparticles, revealing that c-BN exhibits superior thermal stability and enhanced self-healing capability, originating from the higher kinetic barriers associated with partially ionic B–N bonds relative to covalent C–C bonds.