Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach-Reference-Cited by-同舟云学术

Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

Published:2021 Issue:38 Volume:9 Page:21723-21731
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Song Siwei¹^ORCID,Chen Fang¹,Wang Yi¹^ORCID,Wang Kangcai¹,Yan Mi¹,Zhang Qinghua¹^ORCID

Affiliation:

1. Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, P. R. China

Abstract

Potential energetic melt-castable materials were screened out by machine-learning assisted high-throughput virtual screening from a generated chemical space, then eight compounds were synthesized and characterized.

Funder

National Natural Science Foundation of China

Science Challenge Project

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/TA/D1TA04441A

Reference48 articles.

1. What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

2. High-throughput virtual screening

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

4. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

5. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

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