dc.contributor.author | Erlebach, Andreas | |
dc.contributor.author | Nachtigall, Petr | |
dc.contributor.author | Grajciar, Lukáš | |
dc.date.accessioned | 2023-05-23T10:10:33Z | |
dc.date.available | 2023-05-23T10:10:33Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14178/1899 | |
dc.description.abstract | The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computational feasible protocol for identification of hypothetical siliceous frameworks that could be targeted experimentally is a great challenge. To tackle this challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain DFT accuracy for thermodynamic stabilities, vibrational properties, as well as reactive and non-reactive phase transformations. As a showcase, we screened an existing zeolite database and revealed >20k additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of siliceous zeolites. | en |
dc.language.iso | en | |
dc.relation.url | https://doi.org/10.1038/s41524-022-00865-w | |
dc.rights | Creative Commons Uveďte původ 4.0 International | cs |
dc.rights | Creative Commons Attribution 4.0 International | en |
dc.title | Accurate large-scale simulations of siliceous zeolites by neural network potentials | en |
dcterms.accessRights | openAccess | |
dcterms.license | https://creativecommons.org/licenses/by/4.0/legalcode | |
dc.date.updated | 2024-01-25T15:10:38Z | |
dc.subject.keyword | initio molecular-dynamics | en |
dc.subject.keyword | total-energy calculations | en |
dc.subject.keyword | phase-transition | en |
dc.subject.keyword | frequency modes | en |
dc.subject.keyword | frameworks | en |
dc.subject.keyword | temperature | en |
dc.subject.keyword | cristobalite | en |
dc.subject.keyword | dispersion | en |
dc.subject.keyword | stability | en |
dc.subject.keyword | pressure | en |
dc.relation.fundingReference | info:eu-repo/grantAgreement/MSM/EF/EF15_003/0000417 | |
dc.relation.fundingReference | info:eu-repo/grantAgreement///PRIMUS/20/SCI/004 | |
dc.relation.fundingReference | info:eu-repo/grantAgreement/GA0/GJ/GJ20-26767Y | |
dc.relation.fundingReference | info:eu-repo/grantAgreement/GA0/GA/GA19-21534S | |
dc.relation.fundingReference | info:eu-repo/grantAgreement/UK/PROGRES/Q46 | |
dc.relation.fundingReference | info:eu-repo/grantAgreement/UK/UNCE/SCI/UNCE/SCI/014 | |
dc.date.embargoStartDate | 2024-01-25 | |
dc.type.obd | 73 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | |
dc.identifier.doi | 10.1038/s41524-022-00865-w | |
dc.identifier.utWos | 000842043500001 | |
dc.identifier.eidScopus | 2-s2.0-85136920410 | |
dc.identifier.obd | 616539 | |
dc.identifier.riv | RIV/00216208:11310/22:10448880 | |
dc.subject.rivPrimary | 10000::10400::10403 | |
dc.relation.datasetUrl | https://doi.org/10.5281/zenodo.5827897 | |
dcterms.isPartOf.name | npj Computational Materials | |
dcterms.isPartOf.issn | 2057-3960 | |
dcterms.isPartOf.journalYear | 2022 | |
dcterms.isPartOf.journalVolume | 8 | |
dcterms.isPartOf.journalIssue | 1 | |
uk.faculty.primaryId | 115 | |
uk.faculty.primaryName | Přírodovědecká fakulta | cs |
uk.faculty.primaryName | Faculty of Science | en |
uk.department.primaryId | 1049 | |
uk.department.primaryName | Katedra fyzikální a makromolekulární chemie | cs |
uk.department.primaryName | Department of Physical and Macromolecular Chemistry | en |
dc.type.obdHierarchyCs | ČLÁNEK V ČASOPISU::článek v časopisu::původní článek | cs |
dc.type.obdHierarchyEn | JOURNAL ARTICLE::journal article::original article | en |
dc.type.obdHierarchyCode | 73::152::206 | en |
uk.displayTitle | Accurate large-scale simulations of siliceous zeolites by neural network potentials | en |