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dc.contributor.authorHong, Seungbum
dc.contributor.authorLiow, Chi Hao
dc.contributor.authorYuk, Jong Min
dc.contributor.authorByon, Hye Ryung
dc.contributor.authorYang, Yongsoo
dc.contributor.authorCho, Eun Ae
dc.contributor.authorYeom, Jiwon
dc.contributor.authorPark, Gun
dc.contributor.authorKang, Hyeonmuk
dc.contributor.authorKim, Seunggu
dc.contributor.authorShim, Yoonsu
dc.contributor.authorNa, Moony
dc.contributor.authorJeong, Chaehwa
dc.contributor.authorHwang, Gyuseong
dc.contributor.authorKim, Hongjun
dc.contributor.authorKim, Hoon
dc.contributor.authorEom, Seongmun
dc.contributor.authorCho, Seongwoo
dc.contributor.authorJun, Hosun
dc.contributor.authorLee, Yongju
dc.contributor.authorBaucour, Arthur
dc.contributor.authorBang, Kihoon
dc.contributor.authorKim, Myungjoon
dc.contributor.authorYun, Seokjung
dc.contributor.authorRyu, Jeongjae
dc.contributor.authorHan, Youngjoon
dc.contributor.authorJetybayeva, Albina
dc.contributor.authorChoi, Pyuck Pa
dc.contributor.authorAgar, Joshua C.
dc.contributor.authorKalinin, Sergei V.
dc.contributor.authorVoorhees, Peter W.
dc.contributor.authorLittlewood, Peter
dc.contributor.authorLee, Hyuck Mo
dc.date.accessioned2024-03-15T12:30:09Z
dc.date.available2024-03-15T12:30:09Z
dc.date.issued2021-03-23
dc.identifier300221796
dc.identifier1cf17a11-31cf-4b9c-b7de-1f76851e3008
dc.identifier85101494302
dc.identifier33577296
dc.identifier.citationHong , S , Liow , C H , Yuk , J M , Byon , H R , Yang , Y , Cho , E A , Yeom , J , Park , G , Kang , H , Kim , S , Shim , Y , Na , M , Jeong , C , Hwang , G , Kim , H , Kim , H , Eom , S , Cho , S , Jun , H , Lee , Y , Baucour , A , Bang , K , Kim , M , Yun , S , Ryu , J , Han , Y , Jetybayeva , A , Choi , P P , Agar , J C , Kalinin , S V , Voorhees , P W , Littlewood , P & Lee , H M 2021 , ' Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration ' , ACS Nano , vol. 15 , no. 3 , pp. 3971-3995 . https://doi.org/10.1021/acsnano.1c00211en
dc.identifier.issn1936-0851
dc.identifier.urihttps://hdl.handle.net/10023/29504
dc.descriptionThis work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.en
dc.description.abstractMultiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.
dc.format.extent25
dc.format.extent4434065
dc.language.isoeng
dc.relation.ispartofACS Nanoen
dc.subjectKAISTen
dc.subjectLi-ion batteryen
dc.subjectM3I3en
dc.subjectMachine learningen
dc.subjectMaterials and molecular modelingen
dc.subjectMaterials imagingen
dc.subjectMaterials informaticsen
dc.subjectMaterials integrationen
dc.subjectMaterials Science(all)en
dc.subjectEngineering(all)en
dc.subjectPhysics and Astronomy(all)en
dc.titleReducing time to discovery : materials and molecular modeling, imaging, informatics, and integrationen
dc.typeJournal itemen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.1021/acsnano.1c00211
dc.description.statusPeer revieweden
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85101494302&partnerID=8YFLogxKen


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