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Abstract
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 model. We expand widely on our initial work [M. A. Caro et al., 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 (sp(2)-rich) to high-density (sp(3)-rich, "diamondlike") amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce sp- and sp(2)-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.
Original language | English |
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Article number | 174201 |
Number of pages | 21 |
Journal | Physical Review B |
Volume | 102 |
Issue number | 17 |
DOIs | |
Publication status | Published - 2 Nov 2020 |
MoE publication type | A1 Journal article-refereed |
Keywords
- MOLECULAR-DYNAMICS SIMULATIONS
- CROSS-SECTIONAL STRUCTURE
- AB-INITIO SIMULATIONS
- REACTIVE FORCE-FIELD
- PLANE-WAVE
- ELECTROCHEMICAL DETECTION
- STRUCTURAL MOTIFS
- TOTAL-ENERGY
- GROWTH
- POTENTIALS
Fingerprint
Dive into the research topics of 'Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon'. Together they form a unique fingerprint.Datasets
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Amorphous carbon films generated through simulated deposition with GAP from 1eV to 100eV
Caro Bayo, M. (Creator), Zenodo, 15 Jun 2020
Dataset
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NEXTCELL: Next generation interatomic potentials to simulate new cellulose based materials
Caro, M. (Principal investigator)
01/09/2020 → 31/08/2025
Project: Academy of Finland: Other research funding
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Accurate computational electrochemistry from density functional theory and multiscale
Caro, M. (Principal investigator)
01/09/2017 → 31/08/2020
Project: Academy of Finland: Other research funding