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

Authors :
Jiwon Yeom
EunAe Cho
Peter B. Littlewood
Peter W. Voorhees
Hyuck Mo Lee
Chaehwa Jeong
Jeongjae Ryu
Sergei V. Kalinin
Albina Jetybayeva
Pyuck-Pa Choi
Moony Na
Yongju Lee
Arthur Baucour
Yoon Su Shim
Hoon Kim
Seongmun Eom
Seunggu Kim
Kihoon Bang
Hosun Jun
Yongsoo Yang
Joshua C. Agar
Youngjoon Han
Chi Hao Liow
Seokjung Yun
Gyuseong Hwang
Jong Min Yuk
Gun Park
Hyeonmuk Kang
Hye Ryung Byon
Seungbum Hong
Myungjoon Kim
Hongjun Kim
Seongwoo Cho
Source :
ACS Nano. 15:3971-3995
Publication Year :
2021
Publisher :
American Chemical Society (ACS), 2021.

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 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.

Details

ISSN :
1936086X and 19360851
Volume :
15
Database :
OpenAIRE
Journal :
ACS Nano
Accession number :
edsair.doi.dedup.....59437aeb09b356b4e247bdab841a30e8