DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jungwon | ko |
dc.contributor.author | Kim, Young Min | ko |
dc.contributor.author | Hong, Seonghun | ko |
dc.contributor.author | Han, Byungchan | ko |
dc.contributor.author | Nam, Ki Tae | ko |
dc.contributor.author | Jung, Yousung | ko |
dc.date.accessioned | 2023-05-30T07:03:00Z | - |
dc.date.available | 2023-05-30T07:03:00Z | - |
dc.date.created | 2023-05-30 | - |
dc.date.created | 2023-05-30 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.citation | MATTER, v.6, no.3, pp.677 - 690 | - |
dc.identifier.issn | 2590-2393 | - |
dc.identifier.uri | http://hdl.handle.net/10203/306977 | - |
dc.description.abstract | Colloidal nanoparticles are attractive materials for various energy and chemical applications. Due to their strictly tunable struc-ture-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 nano -particles 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 synthe-sis, automated materials characterization, machine-learning optimi-zation, and computational prediction of desired structure-property relationships. We discuss the need and the current levels of automa-tion in different parts of nanoparticle synthesis experiments with future directions and outlook. | - |
dc.language | English | - |
dc.publisher | CELL PRESS | - |
dc.title | Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning | - |
dc.type | Article | - |
dc.identifier.wosid | 000991291500001 | - |
dc.identifier.scopusid | 2-s2.0-85148695822 | - |
dc.type.rims | ART | - |
dc.citation.volume | 6 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 677 | - |
dc.citation.endingpage | 690 | - |
dc.citation.publicationname | MATTER | - |
dc.identifier.doi | 10.1016/j.matt.2023.01.018 | - |
dc.contributor.localauthor | Jung, Yousung | - |
dc.contributor.nonIdAuthor | Park, Jungwon | - |
dc.contributor.nonIdAuthor | Kim, Young Min | - |
dc.contributor.nonIdAuthor | Hong, Seonghun | - |
dc.contributor.nonIdAuthor | Han, Byungchan | - |
dc.contributor.nonIdAuthor | Nam, Ki Tae | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | automated synthesis | - |
dc.subject.keywordAuthor | characterization | - |
dc.subject.keywordAuthor | closed-loop optimization | - |
dc.subject.keywordAuthor | colloidal nanoparticles | - |
dc.subject.keywordAuthor | computational chemistry | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | MAP1: Discovery | - |
dc.subject.keywordAuthor | synthesis robot | - |
dc.subject.keywordPlus | MICROFLUIDIC SYNTHESIS | - |
dc.subject.keywordPlus | CDSE NANOCRYSTALS | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | EVOLUTION | - |
dc.subject.keywordPlus | DISCOVERY | - |
dc.subject.keywordPlus | EFFICIENT | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | GROWTH | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.