Machine-enabled inverse design of inorganic solid materials: promises and challenges

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Developing high-performance advanced materials requires a deeper insight and search into the chemical space. Until recently, exploration of materials space using chemical intuitions built upon existing materials has been the general strategy, but this direct design approach is often time and resource consuming and poses a significant bottleneck to solve the materials challenges of future sustainability in a timely manner. To accelerate this conventional design process, inverse design, which outputs materials with pre-defined target properties, has emerged as a significant materials informatics platform in recent years by leveraging hidden knowledge obtained from materials data. Here, we summarize the latest progress in machine-enabled inverse materials design categorized into three strategies: high-throughput virtual screening, global optimization, and generative models. We analyze challenges for each approach and discuss gaps to be bridged for further accelerated and rational data-driven materials design.
Publisher
ROYAL SOC CHEMISTRY
Issue Date
2020-05
Language
English
Article Type
Review
Citation

CHEMICAL SCIENCE, v.11, no.19, pp.4871 - 4881

ISSN
2041-6520
DOI
10.1039/d0sc00594k
URI
http://hdl.handle.net/10203/274768
Appears in Collection
CBE-Journal Papers(저널논문)
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