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

Accepted version
Peer-reviewed

Type

Article

Change log

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.

Description

Keywords

3403 Macromolecular and Materials Chemistry, 40 Engineering, 34 Chemical Sciences

Journal Title

Chemistry of Materials

Conference Name

Journal ISSN

0897-4756
1520-5002

Volume Title

30

Publisher

American Chemical Society (ACS)
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)