Machine-enabled inverse design of inorganic solid materials: promises and challenges. Issue 19 (6th May 2020)

• Record Type: Journal Article
• Authors: Noh, Juhwan; Gu, Geun Ho; Kim, Sungwon; Jung, Yousung
• Is Part Of: Chemical science. Volume 11:Issue 19(2020)
• Page Start: 4871
• Page End: 4881
• Publication Date: 2020-05-06
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d0sc00594k
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : The grand challenge of materials science, discovery of novel materials with target properties, can be greatly accelerated by machine-learned inverse design strategies. Abstract : 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.