Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration
Abstract
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 withmore »
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- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea, KAIST Institute for NanoCentury (KINC), Korea Advanced Institute of Science and Engineering (KAIST), Daejeon, 34141, Republic of Korea
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
- Department of Chemistry, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
- Department of Physics, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
- James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1766822
- Alternate Identifier(s):
- OSTI ID: 1785179
- Grant/Contract Number:
- Materials Science and Engineering Division; Office of Science User Facility, Center for Nanoph; AC05-00OR22725
- Resource Type:
- Published Article
- Journal Name:
- ACS Nano
- Additional Journal Information:
- Journal Name: ACS Nano Journal Volume: 15 Journal Issue: 3; Journal ID: ISSN 1936-0851
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; M3I3; materials and molecular modeling; materials imaging; materials informatics; machine learning; materials integration
Citation Formats
Hong, Seungbum, Liow, Chi Hao, Yuk, Jong Min, Byon, Hye Ryung, Yang, Yongsoo, Cho, EunAe, Yeom, Jiwon, Park, Gun, Kang, Hyeonmuk, Kim, Seunggu, Shim, Yoonsu, Na, Moony, Jeong, Chaehwa, Hwang, Gyuseong, Kim, Hongjun, Kim, Hoon, Eom, Seongmun, Cho, Seongwoo, Jun, Hosun, Lee, Yongju, Baucour, Arthur, Bang, Kihoon, Kim, Myungjoon, Yun, Seokjung, Ryu, Jeongjae, Han, Youngjoon, Jetybayeva, Albina, Choi, Pyuck-Pa, Agar, Joshua C., Kalinin, Sergei V., Voorhees, Peter W., Littlewood, Peter, and Lee, Hyuck Mo. Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration. United States: N. p., 2021.
Web. doi:10.1021/acsnano.1c00211.
Hong, Seungbum, Liow, Chi Hao, Yuk, Jong Min, Byon, Hye Ryung, Yang, Yongsoo, Cho, EunAe, Yeom, Jiwon, Park, Gun, Kang, Hyeonmuk, Kim, Seunggu, Shim, Yoonsu, Na, Moony, Jeong, Chaehwa, Hwang, Gyuseong, Kim, Hongjun, Kim, Hoon, Eom, Seongmun, Cho, Seongwoo, Jun, Hosun, Lee, Yongju, Baucour, Arthur, Bang, Kihoon, Kim, Myungjoon, Yun, Seokjung, Ryu, Jeongjae, Han, Youngjoon, Jetybayeva, Albina, Choi, Pyuck-Pa, Agar, Joshua C., Kalinin, Sergei V., Voorhees, Peter W., Littlewood, Peter, & Lee, Hyuck Mo. Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration. United States. https://doi.org/10.1021/acsnano.1c00211
Hong, Seungbum, Liow, Chi Hao, Yuk, Jong Min, Byon, Hye Ryung, Yang, Yongsoo, Cho, EunAe, Yeom, Jiwon, Park, Gun, Kang, Hyeonmuk, Kim, Seunggu, Shim, Yoonsu, Na, Moony, Jeong, Chaehwa, Hwang, Gyuseong, Kim, Hongjun, Kim, Hoon, Eom, Seongmun, Cho, Seongwoo, Jun, Hosun, Lee, Yongju, Baucour, Arthur, Bang, Kihoon, Kim, Myungjoon, Yun, Seokjung, Ryu, Jeongjae, Han, Youngjoon, Jetybayeva, Albina, Choi, Pyuck-Pa, Agar, Joshua C., Kalinin, Sergei V., Voorhees, Peter W., Littlewood, Peter, and Lee, Hyuck Mo. Fri .
"Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration". United States. https://doi.org/10.1021/acsnano.1c00211.
@article{osti_1766822,
title = {Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration},
author = {Hong, Seungbum and Liow, Chi Hao and Yuk, Jong Min and Byon, Hye Ryung and Yang, Yongsoo and Cho, EunAe and Yeom, Jiwon and Park, Gun and Kang, Hyeonmuk and Kim, Seunggu and Shim, Yoonsu and Na, Moony and Jeong, Chaehwa and Hwang, Gyuseong and Kim, Hongjun and Kim, Hoon and Eom, Seongmun and Cho, Seongwoo and Jun, Hosun and Lee, Yongju and Baucour, Arthur and Bang, Kihoon and Kim, Myungjoon and Yun, Seokjung and Ryu, Jeongjae and Han, Youngjoon and Jetybayeva, Albina and Choi, Pyuck-Pa and Agar, Joshua C. and Kalinin, Sergei V. and Voorhees, Peter W. and Littlewood, Peter and Lee, Hyuck Mo},
abstractNote = {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.},
doi = {10.1021/acsnano.1c00211},
journal = {ACS Nano},
number = 3,
volume = 15,
place = {United States},
year = {Fri Feb 12 00:00:00 EST 2021},
month = {Fri Feb 12 00:00:00 EST 2021}
}
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