TY - JOUR
T1 - Reducing time to discovery
T2 - materials and molecular modeling, imaging, informatics, and integration
AU - Hong, Seungbum
AU - Liow, Chi Hao
AU - Yuk, Jong Min
AU - Byon, Hye Ryung
AU - Yang, Yongsoo
AU - Cho, Eun Ae
AU - Yeom, Jiwon
AU - Park, Gun
AU - Kang, Hyeonmuk
AU - Kim, Seunggu
AU - Shim, Yoonsu
AU - Na, Moony
AU - Jeong, Chaehwa
AU - Hwang, Gyuseong
AU - Kim, Hongjun
AU - Kim, Hoon
AU - Eom, Seongmun
AU - Cho, Seongwoo
AU - Jun, Hosun
AU - Lee, Yongju
AU - Baucour, Arthur
AU - Bang, Kihoon
AU - Kim, Myungjoon
AU - Yun, Seokjung
AU - Ryu, Jeongjae
AU - Han, Youngjoon
AU - Jetybayeva, Albina
AU - Choi, Pyuck Pa
AU - Agar, Joshua C.
AU - Kalinin, Sergei V.
AU - Voorhees, Peter W.
AU - Littlewood, Peter
AU - Lee, Hyuck Mo
N1 - This 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.
PY - 2021/3/23
Y1 - 2021/3/23
N2 - Multiscale 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.
AB - Multiscale 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.
KW - KAIST
KW - Li-ion battery
KW - M3I3
KW - Machine learning
KW - Materials and molecular modeling
KW - Materials imaging
KW - Materials informatics
KW - Materials integration
UR - http://www.scopus.com/inward/record.url?scp=85101494302&partnerID=8YFLogxK
U2 - 10.1021/acsnano.1c00211
DO - 10.1021/acsnano.1c00211
M3 - Review article
C2 - 33577296
AN - SCOPUS:85101494302
SN - 1936-0851
VL - 15
SP - 3971
EP - 3995
JO - ACS Nano
JF - ACS Nano
IS - 3
ER -