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Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models

Jin Soo-Ah; Rojas Orlando J.; Rinke Patrick; Todorovic Milica; Kämäräinen Tero

dc.contributor.authorJin Soo-Ah
dc.contributor.authorRojas Orlando J.
dc.contributor.authorRinke Patrick
dc.contributor.authorTodorovic Milica
dc.contributor.authorKämäräinen Tero
dc.date.accessioned2022-10-28T13:25:20Z
dc.date.available2022-10-28T13:25:20Z
dc.identifier.urihttps://www.utupub.fi/handle/10024/165066
dc.description.abstractOxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and pK(a)) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA.
dc.language.isoen
dc.publisherSPRINGER HEIDELBERG
dc.titleMachine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models
dc.identifier.urlhttps://link.springer.com/article/10.1557/s43577-021-00183-4
dc.identifier.urnURN:NBN:fi-fe2022081154320
dc.contributor.organizationfi=materiaalitekniikka|en=Materiaalitekniikka|
dc.contributor.organization-code2610202
dc.converis.publication-id174733582
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/174733582
dc.identifier.eissn1938-1425
dc.identifier.jour-issn0883-7694
dc.okm.affiliatedauthorTodorovic, Milica
dc.okm.discipline114 Fysiikkafi_FI
dc.okm.discipline216 Materials engineeringen_GB
dc.okm.discipline216 Materiaalitekniikkafi_FI
dc.okm.discipline114 Physical sciencesen_GB
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeJournal article
dc.publisher.countrySaksafi_FI
dc.publisher.countryGermanyen_GB
dc.publisher.country-codeDE
dc.relation.doi10.1557/s43577-021-00183-4
dc.relation.ispartofjournalMRS Bulletin
dc.year.issued2022


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