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

Area of Science:

• Materials Science and Engineering
• Computational Materials Science
• Materials Informatics

Background:

• Multiscale and multimodal imaging are crucial for advancing materials theory and design.
• Global initiatives like the Materials Genome Initiative and Materials Informatics are accelerating materials discovery.
• KAIST's Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3) initiative aims to revolutionize materials development.

Purpose of the Study:

• To review global efforts in materials informatics and multiscale imaging.
• To discuss the role of machine learning and imaging in realizing the M3I3 vision.
• To highlight the integration of data mining and scientific insights for materials discovery.

Main Methods:

• Review of photon, electron, and physical probe microscopies for multiscale structural hierarchy analysis.
• Application of machine learning and data mining for identifying materials with improved properties.
• Case study on Ni-Co-Mn cathode materials development to illustrate M3I3's approach.

Main Results:

• Multiscale imaging combined with machine learning accelerates the understanding of structure-property relationships.
• Data mining from literature and machine learning offer a more efficient approach to materials discovery than classical methods.
• Development of libraries for multiscale structure-property-processing relationships is demonstrated.

Conclusions:

• The M3I3 initiative leverages multiscale imaging and machine learning to expedite materials discovery and design.
• Integrated approaches combining experimental data, computational modeling, and informatics are key to future materials innovation.
• The future of materials science lies in synergistic integration of advanced imaging, data science, and theoretical modeling.