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
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.
ACS Nano. 2021 Mar 23;15(3):3971-3995. doi: 10.1021/acsnano.1c00211. Epub 2021 Feb 12.
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.
材料结构与性能的多尺度和多模态成像为材料理论与设计蓬勃发展提供了坚实基础。最近，韩国科学技术院宣布了10个旗舰研究领域，其中包括韩国科学技术院材料革命：材料与分子建模、成像、信息学与集成（M3I3）。M3I3计划旨在通过阐明多尺度加工-结构-性能关系和材料层次结构来缩短材料发现、设计和开发的时间，这些关系和层次结构将通过机器学习与科学见解相结合来进行量化和理解。在本综述中，我们首先介绍全球范围内相关计划的最新进展，如美国的材料基因组计划、材料信息学、材料项目、开放量子材料数据库，日本的信息集成材料研究计划，欧盟的新型材料发现、NOMAD知识库，中国的材料科学数据共享网络，德国的从材料到创新，以及韩国的创意材料发现，并讨论多尺度材料和分子成像与机器学习相结合在实现M3I3愿景中的作用。具体而言，将重新审视使用光子、电子和物理探针的显微镜技术，重点关注多尺度结构层次以及结构-性能关系。此外，与传统方法相比，结合机器学习从文献中进行数据挖掘在寻找具有改进性能的材料结构未来方向方面将更有效。将对能源和信息应用材料的实例进行综述和讨论。以镍钴锰阴极材料的开发为例，说明M3I3创建多尺度结构-性能-加工关系库的方法。我们以对M3I3领域最新发展的未来展望作为结尾。
