From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions
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| Format: | Preprint |
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2026
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| author | Yan, Xiaoli Firestone, Millicent A. Keceli, Murat Chaudhuri, Santanu Huerta, Eliu |
| author_facet | Yan, Xiaoli Firestone, Millicent A. Keceli, Murat Chaudhuri, Santanu Huerta, Eliu |
| contents | 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 HPHT 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 ReaxFF simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release conditions. We further investigate how the initial nanodiamond morphology influences the resulting transformation products. Evolution of nanostructure, allotrope, carbon hybridization, and ring statistics are tracked. 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 layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into full sp2 structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. 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 MLP regressors on over 10^5 node-hours of simulations. The model reliably predicts the number of graphitized layers from T-P trajectories with R^2 exceeding 0.90. Collectively, morphological control combined with optimized quench-decompression conditions promote the selective synthesis of nanocarbon allotropes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_18316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions Yan, Xiaoli Firestone, Millicent A. Keceli, Murat Chaudhuri, Santanu Huerta, Eliu Materials Science Other Condensed Matter 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 HPHT 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 ReaxFF simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release conditions. We further investigate how the initial nanodiamond morphology influences the resulting transformation products. Evolution of nanostructure, allotrope, carbon hybridization, and ring statistics are tracked. 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 layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into full sp2 structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. 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 MLP regressors on over 10^5 node-hours of simulations. The model reliably predicts the number of graphitized layers from T-P trajectories with R^2 exceeding 0.90. Collectively, morphological control combined with optimized quench-decompression conditions promote the selective synthesis of nanocarbon allotropes. |
| title | From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions |
| topic | Materials Science Other Condensed Matter |
| url | https://arxiv.org/abs/2603.18316 |