Structure-property relations in amorphous carbon for photovoltaics

Risplendi, Francesca; Bernardi, Marco; Cicero, Giancarlo; Grossman, Jeffrey C.

doi:10.1063/1.4891498

Cited by 18 publications

(24 citation statements)

References 29 publications

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“…Unfortunately, DFT-MD simulations are several orders of magnitude more costly than classical MD, severely limiting the accessible system sizes and time scales. Carbon is a prime example: different routes to computationally generate ta-C structures have been explored * mcaroba@gmail.com in detail, the most popular being the "liquid quench" technique [10][11][12][13][14][15][16]. Explicit deposition of carbon atoms [17][18][19][20][21][22][23][24][25][26], mimicking ta-C film growth under experimental conditions, is too computationally costly to be practical at the DFT level.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…This functional, despite its simplicity, has long been used for atomistic simulations of a-C and is still the de facto standard for many current applications. 15,27,28 Further work may be concerned with the application of higher-level DFT methods, such as computationally much more expensive hybrid functionals, or the implementation of dispersion correctionsthese will likely be interesting additions to the GAP framework, but are beyond the scope of the present study.…”

Section: A General Protocol For Melt-quench Simulationsmentioning

confidence: 99%

“…On the other hand, seminal studies based on tightbinding schemes [20][21][22] as well as density-functional theory (DFT) [23][24][25][26] early on afforded atomistic structure models of a-C, and more recent DFT-MD studies dealt with applications in photovoltaics 27 or coatings. 28 Furthermore, liquid carbon has been of interest-for example, in first-principles studies of the diamond melting line which is difficult to evaluate experimentally.…”

Section: -6mentioning

confidence: 99%

Machine learning based interatomic potential for amorphous carbon

2017

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We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine-learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a novel hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with state-of-the-art empirical potentials. Exemplary applications of the GAP model to surfaces of "diamond-like" tetrahedral amorphous carbon (ta-C) are presented, including an estimate of the amorphous material's surface energy, and simulations of high-temperature surface reconstructions ("graphitization"). The new interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures.

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“…On Earth, the approach is ideal for facile and inexpensive examination of spatial variations in the chemical composition of microscopic fossils or the regional maturity trends across sedimentary basins in order to identify the best preserved organic matter for more detailed geobiological study. It can be immediately used to determine H:C for OMderived electronic grade thin films and therefore infer their optical properties [38].…”

Section: Introductionmentioning

confidence: 99%

Rapid, direct and non-destructive assessment of fossil organic matter via microRaman spectroscopy

Ferralis

Matys

Knoll

et al. 2016

Carbon

135

110

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Raman spectroscopy is widely used to evaluate the nature and potential origins of carbonaceous matter in Earth's oldest rocks and minerals. It is also the tool that will be used for organic detection on the next vehicles to remotely explore the surface of Mars.Here we present, for the first time, a novel quantitative method in which previously neglected Raman spectral features are correlated directly, linearly, and with excellent accuracy, to the microchemistry of carbonaceous materials through the elemental H:C ratio, regardless of contamination. We show applicability and predictive capabilities of this methodology in evaluating H:C ratios between 0.01 and 0.65 in Archean and type III kerogens. We demonstrate its application to chemical microRaman mapping by statistical analysis of a 750Ma microfossil and its encompassing sediments. Raman-derived H:C data can also be used to estimate the degree to which kerogen C-isotopic data has been shifted from its original values due to the effects of metamorphism. The new methodology directly and non-invasively affords spatially resolved assessments of organic matter preservation and microscale chemical diversity within any geologically 2 preserved terrestrial or extraterrestrial sample, including in the use of organic matter in technological applications.

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Structure-property relations in amorphous carbon for photovoltaics

Cited by 18 publications

References 29 publications

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

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

Machine learning based interatomic potential for amorphous carbon

Rapid, direct and non-destructive assessment of fossil organic matter via microRaman spectroscopy

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