Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon

Journal article

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.

Authors: Caro, MA; Csányi, G; Laurila, T; Deringer, VL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Physical Society
• Journal: Physical Review B: Condensed matter and materials physics
• Volume: 102
• Issue: 17
• Article number: 174201
• Publication date: 2020-11-02
• Acceptance date: 2020-10-12
• DOI: 10.1103/PhysRevB.102.174201
• EISSN: 1550-235X
• ISSN: 1098-0121
• Language: English
• Keywords: FFR