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Machine-enabled inverse design of inorganic solid materials: promises and challenges
Juhwan Noh1, Geun Ho Gu1, Sungwon Kim1
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea ysjn@kaist.ac.kr.
Chemical Science
|June 14, 2021
Summary
Inverse design accelerates the discovery of advanced materials with desired properties by leveraging data-driven approaches. This approach overcomes limitations of traditional methods, enabling faster solutions for sustainability challenges.
Area of Science:
- Materials Science
- Computational Chemistry
- Materials Informatics
Background:
- Traditional materials development relies on intuition and is slow, hindering progress on sustainability.
- Exploring the vast chemical space for high-performance materials is a significant bottleneck.
Purpose of the Study:
- To summarize recent advancements in machine-enabled inverse materials design.
- To analyze the challenges and future directions for data-driven materials discovery.
Main Methods:
- Review of machine-enabled inverse design strategies.
- Categorization into high-throughput virtual screening, global optimization, and generative models.
Main Results:
- Machine-enabled inverse design offers a powerful alternative to conventional materials development.
- Identified key challenges within each inverse design strategy.
Conclusions:
- Inverse design is crucial for accelerating the discovery of advanced materials.
- Further research is needed to bridge gaps for more rational and efficient data-driven design.
