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dc.contributor.authorKapil, Venkat
dc.contributor.authorEngel, Edgar
dc.date.accessioned2022-03-14T02:04:50Z
dc.date.available2022-03-14T02:04:50Z
dc.date.issued2022-02-08
dc.identifier.issn0027-8424
dc.identifier.otherPMC8832981
dc.identifier.other35131847
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334950
dc.description.abstractPredictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for general molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient "end to end" framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds-benzene, glycine, and succinic acid-and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task.
dc.languageeng
dc.publisherProceedings of the National Academy of Sciences
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 7505876
dc.sourceessn: 1091-6490
dc.subjectPolymorphism
dc.subjectMachine Learning
dc.subjectStatistical Mechanics
dc.subjectAb Initio Thermodynamics
dc.titleA complete description of thermodynamic stabilities of molecular crystals.
dc.typeArticle
dc.date.updated2022-03-14T02:04:49Z
prism.issueIdentifier6
prism.publicationNameProc Natl Acad Sci U S A
prism.volume119
dc.identifier.doi10.17863/CAM.82388
dcterms.dateAccepted2021-12-23
rioxxterms.versionofrecord10.1073/pnas.2111769119
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidKapil, Venkat [0000-0003-0324-2198]
dc.contributor.orcidEngel, Edgar [0000-0003-2944-9445]
dc.identifier.eissn1091-6490
pubs.funder-project-idSwiss National Science Foundation (P2ELP2_191678, 191678)
pubs.funder-project-idTrinity College, University of Cambridge (Junior Research Fellowship)
cam.issuedOnline2022-02-07


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