Machine-enabled inverse design of inorganic solid materials: promises and challenges-Reference-Cited by-同舟云学术

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

Published:2020 Issue:19 Volume:11 Page:4871-4881
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Noh Juhwan¹²³⁴^ORCID,Gu Geun Ho¹²³⁴,Kim Sungwon¹²³⁴,Jung Yousung¹²³⁴^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering

2. Korea Advanced Institute of Science and Technology (KAIST)

3. Daejeon 34141

4. Republic of Korea

Abstract

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

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/SC/D0SC00594K

Reference98 articles.

1. The 2019 materials by design roadmap

2. Inverse design in search of materials with target functionalities

3. Inverse molecular design using machine learning: Generative models for matter engineering

4. Deep learning for molecular design—a review of the state of the art

5. Computational materials design of crystalline solids

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