Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-11-02
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en
Pages
21
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Physical Review B, Volume 102, issue 17
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.Description
Keywords
MOLECULAR-DYNAMICS SIMULATIONS, CROSS-SECTIONAL STRUCTURE, AB-INITIO SIMULATIONS, REACTIVE FORCE-FIELD, PLANE-WAVE, ELECTROCHEMICAL DETECTION, STRUCTURAL MOTIFS, TOTAL-ENERGY, GROWTH, POTENTIALS
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Citation
Caro, M A, Csanyi, G, Laurila, T & Deringer, V L 2020, ' Machine learning driven simulated deposition of carbon films : From low-density to diamondlike amorphous carbon ', Physical Review B, vol. 102, no. 17, 174201 . https://doi.org/10.1103/PhysRevB.102.174201