Show simple item record

dc.contributor.authorWengert, Simonen
dc.contributor.authorCsányi, Gáboren
dc.contributor.authorReuter, Karstenen
dc.contributor.authorMargraf, Johannes Ten
dc.date.accessioned2021-04-26T23:31:18Z
dc.date.available2021-04-26T23:31:18Z
dc.date.issued2021-02-11en
dc.identifier.issn2041-6520
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/321618
dc.description.abstractThe combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level—for which an efficient implementation is available—with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP.
dc.format.mediumElectronicen
dc.languageengen
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleData-efficient machine learning for molecular crystal structure prediction.en
dc.typeArticle
prism.endingPage4546
prism.issueIdentifier12en
prism.publicationDate2021en
prism.publicationNameChemical scienceen
prism.startingPage4536
prism.volume12en
dc.identifier.doi10.17863/CAM.68736
dcterms.dateAccepted2021-02-05en
rioxxterms.versionofrecord10.1039/d0sc05765gen
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-02-11en
dc.contributor.orcidCsányi, Gábor [0000-0002-8180-2034]
dc.contributor.orcidMargraf, Johannes T [0000-0002-0862-5289]
dc.identifier.eissn2041-6539
rioxxterms.typeJournal Article/Reviewen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International