2026-03-16 アルゴンヌ国立研究所（ANL）
Evolution pathways of post-detonation nanodiamonds into carbon nano-onions and carbon dots under extreme thermodynamic conditions following a detonation event. Structures are based on exascale molecular dynamics simulations and visually enhanced with AI to optimize the spatial arrangement, lighting and aesthetics. (Image by Eliu Huerta & Xiaoli Yan, Data Science and Learning division/Argonne National Laboratory.)
＜関連情報＞
- https://www.anl.gov/article/nanodiamonds-and-beyond-designing-carbon-materials-with-artificial-intelligence-at-exascale
- https://www.sciencedirect.com/science/article/abs/pii/S0008622326001405
原子モデルから機械学習まで：極限条件下におけるナノカーボンの予測設計 From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions
Xiaoli Yan, Millicent A. Firestone, Murat Keçeli, Santanu Chaudhuri, Eliu Huerta
Carbon Available online: 26 February 2026
DOI:https://doi.org/10.1016/j.carbon.2026.121366
Abstract
The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp2-hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp2-hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 105 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R2) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.
