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dc.contributor.authorHuang, Wenxuan
dc.contributor.authorUrban, Alexander
dc.contributor.authorRong, Ziqin
dc.contributor.authorDing, Zhiwei
dc.contributor.authorLuo, Chuan
dc.contributor.authorCeder, Gerbrand
dc.date.accessioned2018-03-05T17:30:05Z
dc.date.available2018-03-05T17:30:05Z
dc.date.issued2017-12
dc.identifier.citationHuang , W , Urban , A , Rong , Z , Ding , Z , Luo , C & Ceder , G 2017 , ' Construction of ground-state preserving sparse lattice models for predictive materials simulations ' npj Computational Materials , vol. 3 , 30 . https://doi.org/10.1038/s41524-017-0032-0en
dc.identifier.issn2057-3960
dc.identifier.otherPURE: 252459763
dc.identifier.otherPURE UUID: 00f9d334-5d60-4a1f-94bb-68c127c9a4d8
dc.identifier.otherScopus: 85042212795
dc.identifier.otherORCID: /0000-0002-9021-279X/work/42504541
dc.identifier.urihttp://hdl.handle.net/10023/12852
dc.descriptionThis work was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571.en
dc.description.abstractFirst-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2x Fe2(1-x)O2 and Li2x Ti2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofnpj Computational Materialsen
dc.rights© The Author(s) 2017. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en
dc.subjectQD Chemistryen
dc.subjectMaterials Science(all)en
dc.subjectComputer Science Applicationsen
dc.subjectModelling and Simulationen
dc.subjectMechanics of Materialsen
dc.subjectDASen
dc.subject.lccQDen
dc.titleConstruction of ground-state preserving sparse lattice models for predictive materials simulationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Chemistryen
dc.identifier.doihttps://doi.org/10.1038/s41524-017-0032-0
dc.description.statusPeer revieweden


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