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

TL;DR

This study uses machine learning-based simulations to explore the atomic-scale growth of amorphous carbon films across a range of densities, revealing different impact mechanisms and elastic properties.

Contribution

It introduces a ML-driven simulation approach to model the deposition of amorphous carbon films over a broad density spectrum, expanding previous work and providing new insights into their atomic structures.

Findings

Low-energy impacts lead to sp- and sp2-rich growth.

High-energy impacts cause peening effects.

The scheme for computing anisotropic elastic properties is effective.

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 (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.