Literature DB >> 33577296

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

Seungbum Hong1,2, Chi Hao Liow1, Jong Min Yuk1, Hye Ryung Byon3, Yongsoo Yang4, EunAe Cho1, Jiwon Yeom1, Gun Park1, Hyeonmuk Kang1, Seunggu Kim3, Yoonsu Shim1, Moony Na3, Chaehwa Jeong4, Gyuseong Hwang1, Hongjun Kim1, Hoon Kim1, Seongmun Eom1, Seongwoo Cho1, Hosun Jun1, Yongju Lee1, Arthur Baucour1, Kihoon Bang1, Myungjoon Kim1, Seokjung Yun1, Jeongjae Ryu1, Youngjoon Han1, Albina Jetybayeva1, Pyuck-Pa Choi1, Joshua C Agar5, Sergei V Kalinin6, Peter W Voorhees7, Peter Littlewood8, Hyuck Mo Lee1.   

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.

Entities:  

Keywords:  KAIST; Li-ion battery; M3I3; machine learning; materials and molecular modeling; materials imaging; materials informatics; materials integration

Year:  2021        PMID: 33577296     DOI: 10.1021/acsnano.1c00211

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  3 in total

1.  The materials tetrahedron has a "digital twin".

Authors:  Michael E Deagen; L Catherine Brinson; Richard A Vaia; Linda S Schadler
Journal:  MRS Bull       Date:  2022-02-01       Impact factor: 4.882

2.  Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks.

Authors:  Lijun Zhang; Shaojin Wang; Xinhua Zhu; Xiaohui Guo; Yuanbing Gu
Journal:  Comput Intell Neurosci       Date:  2022-06-23

Review 3.  A Generative Approach to Materials Discovery, Design, and Optimization.

Authors:  Dhruv Menon; Raghavan Ranganathan
Journal:  ACS Omega       Date:  2022-07-24
  3 in total

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