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

Chemical Science
Juhwan NohYousung Jung

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.

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Citations

Jun 15, 2021·ACS Omega·Kan Hatakeyama-Sato, Kenichi Oyaizu
Jul 10, 2021·The Journal of Chemical Physics·Julia WestermayrReinhard J Maurer
Aug 4, 2021·The Journal of the Acoustical Society of America·Tristan ShahPeter Gerstoft
Aug 17, 2021·Soft Matter·Kianoosh SattariJian Lin
Aug 24, 2021·Journal of Chemical Information and Modeling·Dong Hyeon Mok, Seoin Back
Sep 3, 2020·ACS Central Science·Sungwon KimYousung Jung
Sep 12, 2021·Proceedings of the National Academy of Sciences of the United States of America·Lusann YangJohn M Gregoire
Sep 17, 2021·Chemical Reviews·Teo LombardoAlejandro A Franco

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Software Mentioned

CGCNN
AlphaFold
HTVS
Automatic FLOW ( AFLOW )
SELFIES
EA
Seq2Seq
PointNet
SMILES

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