Issue 19, 2020

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

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.

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

Article information

Article type
Minireview
Submitted
31 yan 2020
Accepted
07 apr 2020
First published
15 apr 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2020,11, 4871-4881

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

J. Noh, G. H. Gu, S. Kim and Y. Jung, Chem. Sci., 2020, 11, 4871 DOI: 10.1039/D0SC00594K

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