Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

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dc.contributor.authorHong, Seungbumko
dc.contributor.authorLiow, Chi Haoko
dc.contributor.authorYuk, Jong Minko
dc.contributor.authorByon, Hye Ryungko
dc.contributor.authorYang, Yongsooko
dc.contributor.authorCho, EunAeko
dc.contributor.authorYeom, Jiwonko
dc.contributor.authorPark, Gunko
dc.contributor.authorKang, Hyeonmukko
dc.contributor.authorKim, Seungguko
dc.contributor.authorShim, Yoonsuko
dc.contributor.authorNa, Moonyko
dc.contributor.authorJeong, Chaehwako
dc.contributor.authorHwang, Gyuseongko
dc.contributor.authorKim, Hongjunko
dc.contributor.authorKim, Hoonko
dc.contributor.authorEom, Seongmunko
dc.contributor.authorCho, Seongwooko
dc.contributor.authorJun, Hosunko
dc.contributor.authorLee, Yongjuko
dc.contributor.authorBaucour, Arthurko
dc.contributor.authorBang, Kihoonko
dc.contributor.authorKim, Myungjoonko
dc.contributor.authorYun, Seokjungko
dc.contributor.authorRyu, Jeongjaeko
dc.contributor.authorHan, Youngjoonko
dc.contributor.authorJetybayeva, Albinako
dc.contributor.authorChoi, Pyuck-Pako
dc.contributor.authorAgar, Joshua C.ko
dc.contributor.authorKalinin, Sergei, Vko
dc.contributor.authorVoorhees, Peter W.ko
dc.contributor.authorLittlewood, Peterko
dc.contributor.authorLee, Hyuck Moko
dc.date.accessioned2021-05-04T10:11:12Z-
dc.date.available2021-05-04T10:11:12Z-
dc.date.created2021-05-04-
dc.date.created2021-05-04-
dc.date.created2021-05-04-
dc.date.created2021-05-04-
dc.date.issued2021-02-
dc.identifier.citationACS NANO, v.15, no.3, pp.3971 - 3995-
dc.identifier.issn1936-0851-
dc.identifier.urihttp://hdl.handle.net/10203/282805-
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.languageEnglish-
dc.publisherAMER CHEMICAL SOC-
dc.titleReducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration-
dc.typeArticle-
dc.identifier.wosid000634569100023-
dc.identifier.scopusid2-s2.0-85101494302-
dc.type.rimsART-
dc.citation.volume15-
dc.citation.issue3-
dc.citation.beginningpage3971-
dc.citation.endingpage3995-
dc.citation.publicationnameACS NANO-
dc.identifier.doi10.1021/acsnano.1c00211-
dc.contributor.localauthorHong, Seungbum-
dc.contributor.localauthorYuk, Jong Min-
dc.contributor.localauthorByon, Hye Ryung-
dc.contributor.localauthorYang, Yongsoo-
dc.contributor.localauthorCho, EunAe-
dc.contributor.localauthorChoi, Pyuck-Pa-
dc.contributor.localauthorLee, Hyuck Mo-
dc.contributor.nonIdAuthorLiow, Chi Hao-
dc.contributor.nonIdAuthorHwang, Gyuseong-
dc.contributor.nonIdAuthorHan, Youngjoon-
dc.contributor.nonIdAuthorJetybayeva, Albina-
dc.contributor.nonIdAuthorAgar, Joshua C.-
dc.contributor.nonIdAuthorKalinin, Sergei, V-
dc.contributor.nonIdAuthorVoorhees, Peter W.-
dc.contributor.nonIdAuthorLittlewood, Peter-
dc.description.isOpenAccessN-
dc.type.journalArticleReview-
dc.subject.keywordAuthorM3I3-
dc.subject.keywordAuthormaterials and molecular modeling-
dc.subject.keywordAuthormaterials imaging-
dc.subject.keywordAuthormaterials informatics-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthormaterials integration-
dc.subject.keywordAuthorLi-ion battery-
dc.subject.keywordAuthorKAIST-
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MS-Journal Papers(저널논문)CH-Journal Papers(저널논문)PH-Journal Papers(저널논문)
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