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

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • University of Cambridge
  • University of Freiburg


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.


Original languageEnglish
Pages (from-to)7438–7445
Number of pages8
JournalChemistry of Materials
Issue number21
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

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