| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Csányi, Gábor
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2023Structural Dynamics Descriptors for Metal Halide Perovskites.
- 2023Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.
- 2021Origins of structural and electronic transitions in disordered silicon.
- 2020An accurate and transferable machine learning potential for carbon.
- 2018Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learningcitations
- 2018Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theorycitations
- 2018Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carboncitations
- 2018Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon.
- 2018Reactivity of Amorphous Carbon Surfacescitations
- 2018Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.
- 2017Polytypism in the ground state structure of the Lennard-Jonesium.
- 2017Polytypism in the ground state structure of the Lennard-Jonesiumcitations
- 2015Low Speed Crack Propagation via Kink Formation and Advance on the Silicon (110) Cleavage Planecitations
Places of action
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article
Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
Abstract
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.