Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machi...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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American Physical Society
2020
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| _version_ | 1826281383935344640 |
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| author | Caro, MA Csányi, G Laurila, T Deringer, VL |
| author_facet | Caro, MA Csányi, G Laurila, T Deringer, VL |
| author_sort | Caro, MA |
| collection | OXFORD |
| description | Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machine learning (ML) based Gaussian Approximation Potential (GAP) model. We expand widely on our initial work [Phys. Rev. Lett. 120, 166101 (2018)] by now considering a broad range of incident ion energies, thus modeling samples that span the entire range from low-density (sp2 -rich) to high-density (sp3 -rich, “diamond-like”) amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce sp- and sp2 -dominated growth directly around the impact site, whereas high-energy impacts induce peening. Furthermore, we propose and apply a scheme for computing the anisotropic elastic properties of the a-C films. Our work provides fundamental insight into this intriguing class of disordered solids, as well as a conceptual and methodological blueprint for simulating the atomic-scale deposition of other materials with ML-driven molecular dynamics. |
| first_indexed | 2024-03-07T00:27:58Z |
| format | Journal article |
| id | oxford-uuid:7ec62d32-e327-4fec-8ea2-e16b778eda52 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:27:58Z |
| publishDate | 2020 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | oxford-uuid:7ec62d32-e327-4fec-8ea2-e16b778eda522022-03-26T21:12:22ZMachine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbonJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7ec62d32-e327-4fec-8ea2-e16b778eda52EnglishSymplectic ElementsAmerican Physical Society2020Caro, MACsányi, GLaurila, TDeringer, VLAmorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machine learning (ML) based Gaussian Approximation Potential (GAP) model. We expand widely on our initial work [Phys. Rev. Lett. 120, 166101 (2018)] by now considering a broad range of incident ion energies, thus modeling samples that span the entire range from low-density (sp2 -rich) to high-density (sp3 -rich, “diamond-like”) amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce sp- and sp2 -dominated growth directly around the impact site, whereas high-energy impacts induce peening. Furthermore, we propose and apply a scheme for computing the anisotropic elastic properties of the a-C films. Our work provides fundamental insight into this intriguing class of disordered solids, as well as a conceptual and methodological blueprint for simulating the atomic-scale deposition of other materials with ML-driven molecular dynamics. |
| spellingShingle | Caro, MA Csányi, G Laurila, T Deringer, VL Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title | Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title_full | Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title_fullStr | Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title_full_unstemmed | Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title_short | Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon |
| title_sort | machine learning driven simulated deposition of carbon films from low density to diamondlike amorphous carbon |
| work_keys_str_mv | AT caroma machinelearningdrivensimulateddepositionofcarbonfilmsfromlowdensitytodiamondlikeamorphouscarbon AT csanyig machinelearningdrivensimulateddepositionofcarbonfilmsfromlowdensitytodiamondlikeamorphouscarbon AT laurilat machinelearningdrivensimulateddepositionofcarbonfilmsfromlowdensitytodiamondlikeamorphouscarbon AT deringervl machinelearningdrivensimulateddepositionofcarbonfilmsfromlowdensitytodiamondlikeamorphouscarbon |