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

Tetrahedral amorphous carbon (ta-C) is widely
used for coatings because of 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.