From Atomistic Surface Chemistry to Nanocrystals of Functional Chalcogenides

Deringer, Volker L.; Dronskowski, Richard

doi:10.1002/anie.201506874

Cited by 20 publications

(20 citation statements)

References 86 publications

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“…System-size dependence of ta-C slab models and a quantitative measure for their local structure. (a) Count of sp 3 atoms in the center of the slabs, as in Figure 2c, but now as a function of system size. (b) Same for the sp 2 count at the surfaces.…”

Section: Resultsmentioning

confidence: 99%

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

et al. 2018

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Tetrahedral amorphous carbon (ta-C) is widely used for coatings due to its superior mechanical properties and has been suggested as an electrode material for detecting biomolecules. Despite extensive research, however, the complex atomic-scale structures and chemical reactivity of ta-C surfaces are incompletely understood. Here, we combine machine learning, density-functional tight-binding, and density-functional theory simulations to shed new light on this long-standing problem. We make atomistic models of ta-C surfaces, characterize them by local structural fingerprints, and provide a library of structures at different system sizes. We then move beyond the pure element and exemplify how chemical reactivity (hydrogenation and oxidation) can be modeled at the surfaces. Our work opens up new perspectives for modeling the surfaces and interfaces of amorphous solids, which will advance studies of ta-C and other functional materials.

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Section: Resultsmentioning

confidence: 99%

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

et al. 2018

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“…86 Likewise, the surfaces of amorphous matter are of broad interest but pose particular and significant challenges for modeling. We here present initial applications of our GAP to amorphous carbon surfaces of the 3.0 g cm −3 phase (ta-C).…”

Section: F From the Bulk To Surfacesmentioning

confidence: 99%

“…Realistic materials modeling, especially at the nanoscale, must extend from the bulk to a description of crystal surfaces and their reactivity. 86 Likewise, the surfaces of amorphous matter are of broad interest but pose particular and significant challenges for modeling. We here present initial applications of our GAP to amorphous carbon surfaces of the 3.0 g cm −3 phase (ta-C).…”

Section: F From the Bulk To Surfacesmentioning

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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“…Surfaces have always been paramount to the electronic and photophysical quality and hence practical utility of semiconductors. This is particularly true for nanoscale semiconductors such as colloidal semiconductor nanocrystals (NCs) with inherently high surface-to-volume ratios. − Conventional semiconductor NCs, such as CdSe and InP, need to be overcoated with epitaxial shells (typically ZnS) for eliminating the localized surface states that act as traps for photogenerated electrons and holes, thus diminishing the photoluminescence quantum yields (PL QYs). Newcomers among highly luminescent semiconductor NCs (colloidal NCs of lead halide perovskites [APbX 3 NCs, A = Cs, formamidinium; X = Cl, Br, I, and mixtures thereof] − ) generally offer a much reduced density of surface trap states and greater tolerance of them .…”

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Rationalizing and Controlling the Surface Structure and Electronic Passivation of Cesium Lead Halide Nanocrystals

et al. 2018

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Colloidal lead halide perovskite nanocrystals (NCs) have recently emerged as versatile photonic sources. Their processing and luminescent properties are challenged by the lability of their surfaces, i.e., the interface of the NC core and the ligand shell. On the example of CsPbBr3 NCs, we model the nanocrystal surface structure and its effect on the emergence of trap states using density functional theory. We rationalize the typical observation of a degraded luminescence upon aging or the luminescence recovery upon postsynthesis surface treatments. The conclusions are corroborated by the elemental analysis. We then propose a strategy for healing the surface trap states and for improving the colloidal stability by the combined treatment with didodecyldimethylammonium bromide and lead bromide and validate this approach experimentally. This simple procedure results in robust colloids, which are highly pure and exhibit high photoluminescence quantum yields of up to 95–98%, retained even after three to four rounds of washing.

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From Atomistic Surface Chemistry to Nanocrystals of Functional Chalcogenides

Cited by 20 publications

References 86 publications

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

Machine learning based interatomic potential for amorphous carbon

Rationalizing and Controlling the Surface Structure and Electronic Passivation of Cesium Lead Halide Nanocrystals

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