Projects per year
Abstract
The thermal stability of fullerenes plays a fundamental role in their synthesis and in their thermodynamic and kinetic properties. Here, we perform extensive molecular dynamics (MD) simulations using an accurate machine-learning-based Gaussian Approximation Potential (GAP-20) force field to investigate the energetic and thermal properties of the entire set of 1812 C60 isomers. Our MD simulations predict a comprehensive and quantitative correlation between the relative isomerization energy distribution of the C60 isomers and their thermal fragmentation temperatures. We find that the 1812 C60 isomers span over an energetic range of over 400 kcal mol−1, where the majority of isomers (∼85%) lie in the range between 90 and 210 kcal mol−1 above the most stable C60-𝐼h buckminsterfullerene. Notably, the MD simulations show a clear statistical correlation between the relative energies of the C60 isomers and their fragmentation temperature. The maximum fragmentation temperature is 4800 K for the C60-𝐼h isomer and 3700 K for the energetically least stable isomer, where nearly 80% of isomers lie in a temperature window of 4000–4500 K. In addition, an Arrhenius-based approach is used to map the timescale gap between simulation and experiment and establish a connection between the MD simulations and fragmentation temperatures.
Original language | English |
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Article number | 064302 |
Journal | Journal of Applied Physics |
Volume | 132 |
Issue number | 6 |
DOIs | |
Publication status | Published - 14 Aug 2022 |
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Dive into the research topics of 'Comprehensive theoretical study of the correlation between the energetic and thermal stabilities for the entire set of 1812 C60 isomers'. Together they form a unique fingerprint.Projects
- 1 Finished
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High-level quantum chemistry: From theory to applications
Karton, A. (Investigator 01)
ARC Australian Research Council
27/12/17 → 28/02/22
Project: Research
Research output
- 1 Citations
- 1 Article
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Correlation between the energetic and thermal properties of C40 fullerene isomers: An accurate machine-learning force field study
Aghajamali, A. & Karton, A., Apr 2022, In: Micro and Nano Engineering. 14, 100105.Research output: Contribution to journal › Article › peer-review
Open Access6 Citations (Scopus)