Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
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Authors
Jana, R
Aarva, A
Csányi, G
Pastewka, L
Publication Date
2018Journal Title
Chemistry of Materials
ISSN
0897-4756
Publisher
American Chemical Society (ACS)
Volume
30
Issue
21
Pages
7438-7445
Type
Article
Metadata
Show full item recordCitation
Deringer, V., Caro, M., Jana, R., Aarva, A., Elliott, S., Laurila, T., Csányi, G., & et al. (2018). Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory. Chemistry of Materials, 30 (21), 7438-7445. https://doi.org/10.1021/acs.chemmater.8b02410
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.
Sponsorship
Isaac Newton Trust (1624(n))
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
Engineering and Physical Sciences Research Council (EP/P022596/1)
Identifiers
External DOI: https://doi.org/10.1021/acs.chemmater.8b02410
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285345
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