Machine-learning approach to the design of OSDAs for zeolite beta

Simple item page
dc.citation.firstpage3413
dc.citation.issueNumber9
dc.citation.journalTitleProceedings of the National Academy of Sciences
dc.citation.lastpage3418
dc.citation.volumeNumber116
dc.contributor.authorDaeyaert, Frits
dc.contributor.authorYe, Fengdan
dc.contributor.authorDeem, Michael W.
dc.date.accessioned2019-12-05T18:52:33Z
dc.date.available2019-12-05T18:52:33Z
dc.date.issued2019
dc.description.abstractWe report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below −17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta.
dc.identifier.citationDaeyaert, Frits, Ye, Fengdan and Deem, Michael W.. "Machine-learning approach to the design of OSDAs for zeolite beta." <i>Proceedings of the National Academy of Sciences,</i> 116, no. 9 (2019) National Academy of Sciences: 3413-3418. https://doi.org/10.1073/pnas.1818763116.
dc.identifier.digitalMachine-learning
dc.identifier.doihttps://doi.org/10.1073/pnas.1818763116
dc.identifier.urihttps://hdl.handle.net/1911/107772
dc.language.isoeng
dc.publisherNational Academy of Sciences
dc.rightsThis open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleMachine-learning approach to the design of OSDAs for zeolite beta
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Machine-learning.pdf
Size:
1.23 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Bioengineering Publications
Physics and Astronomy Publications