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

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cam.issuedOnline2018-09-10
dc.contributor.authorDeringer, VL
dc.contributor.authorCaro, MA
dc.contributor.authorJana, R
dc.contributor.authorAarva, A
dc.contributor.authorElliott, SR
dc.contributor.authorLaurila, T
dc.contributor.authorCsányi, G
dc.contributor.authorPastewka, L
dc.contributor.orcidDeringer, VL [0000-0001-6873-0278]
dc.contributor.orcidCaro, MA [0000-0001-9304-4261]
dc.contributor.orcidElliott, SR [0000-0002-8202-8482]
dc.contributor.orcidLaurila, T [0000-0002-1252-8764]
dc.date.accessioned2018-11-17T00:30:49Z
dc.date.available2018-11-17T00:30:49Z
dc.date.issued2018
dc.description.abstractTetrahedral 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.
dc.identifier.doi10.17863/CAM.32713
dc.identifier.eissn1520-5002
dc.identifier.issn0897-4756
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285345
dc.language.isoeng
dc.publisherAmerican Chemical Society (ACS)
dc.publisher.urlhttp://dx.doi.org/10.1021/acs.chemmater.8b02410
dc.subject3403 Macromolecular and Materials Chemistry
dc.subject40 Engineering
dc.subject34 Chemical Sciences
dc.titleComputational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
dc.typeArticle
dcterms.dateAccepted2018-09-10
prism.endingPage7445
prism.issueIdentifier21
prism.publicationDate2018
prism.publicationNameChemistry of Materials
prism.startingPage7438
prism.volume30
pubs.funder-project-idIsaac Newton Trust (1624(n))
pubs.funder-project-idIsaac Newton Trust (17.08(c))
pubs.funder-project-idLeverhulme Trust (ECF-2017-278)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
rioxxterms.licenseref.startdate2018-11-13
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1021/acs.chemmater.8b02410

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