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Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning

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dc.contributor.authorJungwon Park-
dc.contributor.authorKim, Y.M.-
dc.contributor.authorHong, S.-
dc.contributor.authorHan, B.-
dc.contributor.authorNam, K.T.-
dc.contributor.authorJung, Y.-
dc.date.accessioned2023-04-04T22:02:51Z-
dc.date.available2023-04-04T22:02:51Z-
dc.date.created2023-03-06-
dc.date.issued2023-03-
dc.identifier.issn2590-2393-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/13089-
dc.description.abstractColloidal nanoparticles are attractive materials for various energy and chemical applications. Due to their strictly tunable structure-function relationships, reproducibly synthesizing structurally homogeneous nanoparticles is a critical step toward making the nanoparticle technology commercially viable. However, due to a lack of general theoretical foundations for complex nanoparticle formation phenomena, the current synthesis optimizations of nanoparticles are mostly conducted based on the intuitions and trial-and-error-driven manual processes that are slow to explore a large synthesis parameter space. To accelerate these time-consuming and resource-demanding conventional synthesis approaches, we here describe a closed-loop pipeline that consists of robotic synthesis, automated materials characterization, machine-learning optimization, and computational prediction of desired structure-property relationships. We discuss the need and the current levels of automation in different parts of nanoparticle synthesis experiments with future directions and outlook. © 2023 Elsevier Inc.-
dc.language영어-
dc.publisherCell Press-
dc.titleClosed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000991291500001-
dc.identifier.scopusid2-s2.0-85148695822-
dc.identifier.rimsid80046-
dc.contributor.affiliatedAuthorJungwon Park-
dc.identifier.doi10.1016/j.matt.2023.01.018-
dc.identifier.bibliographicCitationMatter, v.6, no.3, pp.677 - 690-
dc.relation.isPartOfMatter-
dc.citation.titleMatter-
dc.citation.volume6-
dc.citation.number3-
dc.citation.startPage677-
dc.citation.endPage690-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusMICROFLUIDIC SYNTHESIS-
dc.subject.keywordPlusCDSE NANOCRYSTALS-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordPlusEVOLUTION-
dc.subject.keywordPlusDISCOVERY-
dc.subject.keywordPlusEFFICIENT-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordPlusGROWTH-
dc.subject.keywordAuthorMAP1: Discovery-
dc.subject.keywordAuthorsynthesis robot-
dc.subject.keywordAuthorautomated synthesis-
dc.subject.keywordAuthorcharacterization-
dc.subject.keywordAuthorclosed-loop optimization-
dc.subject.keywordAuthorcolloidal nanoparticles-
dc.subject.keywordAuthorcomputational chemistry-
dc.subject.keywordAuthormachine learning-
Appears in Collections:
Center for Nanoparticle Research(나노입자 연구단) > 1. Journal Papers (저널논문)
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