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dc.contributor.authorPickard, CJ
dc.date.accessioned2022-06-22T23:30:22Z
dc.date.available2022-06-22T23:30:22Z
dc.date.issued2022
dc.identifier.issn2469-9950
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338302
dc.description.abstractStructure prediction has become a key task of the modern atomistic sciences, and depends on the rapid and reliable computation of energy landscapes. First principles density functional based calculations are highly reliable, faithfully describing entire energy landscapes. They are, however, computationally intensive and slow compared to interatomic potentials. Great progress has been made in the development of machine learning, or data derived, potentials, which promise to de- scribe entire energy landscapes at first principles quality. Compared to first principles approaches, their preparation can be time consuming and delay searching. Ab initio random structure searching (AIRSS) is a straightforward and powerful approach to structure prediction, based on the stochastic generation of sensible initial structures, and their repeated local optimisation. Here, a scheme, com- patible with AIRSS, for the rapid construction of disposable, or ephemeral, data derived potentials (EDDPs) is described. These potentials are constructed using a homogeneous, separable manybody environment vector, and iterative neural network fits, sparsely combined through non-negative least squares. The approach is first tested on methane, boron nitride, elemental boron and urea. In the case of boron, an EDDP generated using data from small unit cells is used to rediscover the com- plex γ-boron structure without recourse to symmetry or fragments. Finally, an EDDP generated for silane (SiH4) at 500 GPa enables the discovery of an extremely complex, dense, structure which significantly modifies silane’s high pressure phase diagram. This has implications for the theoretical exploration for high temperature superconductivity in the dense hydrides, which have so far largely depended on searches in smaller unit cells.
dc.publisherAmerican Physical Society (APS)
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleEphemeral data derived potentials for random structure search
dc.typeArticle
dc.publisher.departmentDepartment of Materials Science And Metallurgy
dc.date.updated2022-06-22T07:25:30Z
prism.publicationNamePhysical Review B
dc.identifier.doi10.17863/CAM.85711
dcterms.dateAccepted2022-06-21
rioxxterms.versionofrecord10.1103/PhysRevB.106.014102
rioxxterms.versionAM
dc.contributor.orcidPickard, Christopher [0000-0002-9684-5432]
dc.identifier.eissn2469-9969
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
pubs.funder-project-idEPSRC (EP/S021981/1)
cam.issuedOnline2022-07-07
cam.orpheus.successWed Aug 03 09:45:53 BST 2022 - Embargo updated
cam.orpheus.counter3
cam.depositDate2022-06-22
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2022-07-07


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