Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential
Multi-shell fullerenes ”buckyonions” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Carbon Trends |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667056922000955 |
| _version_ | 1811155521634304000 |
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| author | C. Ugwumadu K. Nepal R. Thapa Y.G. Lee Y. Al Majali J. Trembly D.A. Drabold |
| author_facet | C. Ugwumadu K. Nepal R. Thapa Y.G. Lee Y. Al Majali J. Trembly D.A. Drabold |
| author_sort | C. Ugwumadu |
| collection | DOAJ |
| description | Multi-shell fullerenes ”buckyonions” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 ∼ 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA, revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjugate gradient energy convergence of the models was achieved with both methods. |
| first_indexed | 2024-04-10T04:34:33Z |
| format | Article |
| id | doaj.art-ea16a914b29446c5a4d8efec62d695b8 |
| institution | Directory Open Access Journal |
| issn | 2667-0569 |
| language | English |
| last_indexed | 2024-04-10T04:34:33Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Carbon Trends |
| spelling | doaj.art-ea16a914b29446c5a4d8efec62d695b82023-03-10T04:36:46ZengElsevierCarbon Trends2667-05692023-03-0110100239Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation PotentialC. Ugwumadu0K. Nepal1R. Thapa2Y.G. Lee3Y. Al Majali4J. Trembly5D.A. Drabold6Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA; Corresponding authors.Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USADepartment of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USADepartment of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USARuss College of Engineering and Technology, Ohio University, Athens, Ohio 45701, USARuss College of Engineering and Technology, Ohio University, Athens, Ohio 45701, USADepartment of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA; Corresponding authors.Multi-shell fullerenes ”buckyonions” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 ∼ 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA, revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjugate gradient energy convergence of the models was achieved with both methods.http://www.sciencedirect.com/science/article/pii/S2667056922000955CarbonBuckyonionFullerenesMachine learningGaussian Approximation Potential |
| spellingShingle | C. Ugwumadu K. Nepal R. Thapa Y.G. Lee Y. Al Majali J. Trembly D.A. Drabold Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential Carbon Trends Carbon Buckyonion Fullerenes Machine learning Gaussian Approximation Potential |
| title | Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential |
| title_full | Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential |
| title_fullStr | Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential |
| title_full_unstemmed | Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential |
| title_short | Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential |
| title_sort | simulation of multi shell fullerenes using machine learning gaussian approximation potential |
| topic | Carbon Buckyonion Fullerenes Machine learning Gaussian Approximation Potential |
| url | http://www.sciencedirect.com/science/article/pii/S2667056922000955 |
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