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

Seungbum Hong*, Chi Hao Liow, Jong Min Yuk, Hye Ryung Byon, Yongsoo Yang, Eun Ae Cho, Jiwon Yeom, Gun Park, Hyeonmuk Kang, Seunggu Kim, Yoonsu Shim, Moony Na, Chaehwa Jeong, Gyuseong Hwang, Hongjun Kim, Hoon Kim, Seongmun Eom, Seongwoo Cho, Hosun Jun, Yongju LeeArthur Baucour, Kihoon Bang, Myungjoon Kim, Seokjung Yun, Jeongjae Ryu, Youngjoon Han, Albina Jetybayeva, Pyuck Pa Choi, Joshua C. Agar, Sergei V. Kalinin, Peter W. Voorhees, Peter Littlewood, Hyuck Mo Lee

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)3971-3995
Number of pages25
JournalACS nano
Volume15
Issue number3
DOIs
StatePublished - Mar 23 2021

Keywords

  • KAIST
  • Li-ion battery
  • M3I3
  • machine learning
  • materials and molecular modeling
  • materials imaging
  • materials informatics
  • materials integration

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)

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