Transferability in interatomic potentials for carbon

Tomás, Carla de; Aghajamali, Alireza; Jones, Jacquelyn; Lim, Daniel J.; López, M. J.; Suarez-Martinez, Irène; Marks, Nigel A.

doi:10.1016/j.carbon.2019.07.074

Cited by 88 publications

(106 citation statements)

References 41 publications

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“…This issue has also been recently highlighted in Ref. [67]. Five-fold coordinated C atoms are considered to be coordination defects, therefore they are highly energetic and one should expect them to not be present in significant numbers in the generated structures.…”

Section: Overcoordinated Atomsmentioning

confidence: 86%

“…These classical potentials are commonly used for MD simulations of large systems, and have been recently reviewed by de Tomas et al [64]. In this context, we may refer the reader to critical discussions of technical aspects of empirical potentials [65,66], and to a benchmark study of various such potentials specifically with a view to quantify their performance for amorphous forms of carbon [67]. We emphasize that a similar benchmark of many empirical potentials is outside the scope of the present work.…”

Section: Comparison With Empirical Interatomic Potentialsmentioning

confidence: 99%

“…3) certainly points to an existing deficiency in the force field but does not necessarily mean that it will not perform well for a different problem (e.g., the graphitization simulations studied in Ref. 67…”

Section: Comparison With Empirical Interatomic Potentialsmentioning

confidence: 99%

“…A critical practical point to raise here is that the improved accuracy of GAP does not come "for free". Indeed, a GAP MD simulation is significantly more CPU expensive to run than CEDIP/ReaxFF (which are both 1-2 orders of magnitude cheaper than GAP) or Tersoff/REBO-II (2-3 orders of magnitude cheaper) [67]. However, for accuracy-critical applications where the only previous option was to run DFT simulations, GAPs and other ML-based interatomic potentials offer the capability to run simulations at similar accuracy but being orders of magnitude cheaper than DFT.…”

Section: Comparison With Empirical Interatomic Potentialsmentioning

confidence: 99%

“…We obtained a GAP-quenched a-C structure containing 25 such 5-c environments, out of a total of 4096 C atoms (0.6%), from the authors of Ref. 67, who brought the issue of 5-fold coordinated C atoms in GAP simulations to our attention. Since computing energy and forces for this structure at the DFT level is impractical, due to its large size, we used a carving technique which involves passivation with H atoms to heal artificially introduced dangling bonds [68].…”

Section: Overcoordinated Atomsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Overcoordinated Atomsmentioning

confidence: 86%