Machine-learning-driven simulated deposition of carbon films: from low-density to diamond-like 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
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 ($sp^{2}$-rich) to high-density
($sp^{3}$-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 $sp^{2}$-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.