Machine learning potential-accelerated multiscale dynamical simulations of nanodiamond structural reconstruction

RH He and JS Dang, JOURNAL OF CHEMICAL PHYSICS, 163, 164310 (2025).

DOI: 10.1063/5.0295427

Atomistic understanding of structural transformations in nanodiamonds (NDs) is vital for manipulating their physicochemical properties, yet remains limited due to the inherent trade-off between simulation accuracy and scale. Here, we develop a machine learning potential (MLP) with density functional theory accuracy and implement it within the deep potential molecular dynamics framework to enable large-scale simulations of NDs comprising 10(3)-10(4) atoms over nanosecond timescales. Our simulations reveal that the transformation dynamics are governed by morphology, surface facets, particle size, and temperature. We identify a multistage transformation pathway, sequentially characterized by outward-in graphitization, inward-out atomic migration, and a subsequent self-healing process, driven by surface energy minimization and internal stress relaxation. These results provide atomistic insight into the evolution of NDs and demonstrate the power of MLP-based approaches for modeling complex, multiscale structural transformations in nanocarbon materials.

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