Quasicrystals predicted and discovered by machine learning-Reference-Cited by-同舟云学术

Quasicrystals predicted and discovered by machine learning

Published:2023-09-25 Issue:9 Volume:7 Page:
ISSN:2475-9953
Container-title:Physical Review Materials
language:en
Short-container-title:Phys. Rev. Materials

Author:

Liu Chang^ORCID,Kitahara Koichi^ORCID,Ishikawa Asuka^ORCID,Hiroto Takanobu^ORCID,Singh Alok^ORCID,Fujita Erina^ORCID,Katsura Yukari^ORCID,Inada Yuki^ORCID,Tamura Ryuji^ORCID,Kimura Kaoru^ORCID,Yoshida Ryo^ORCID

Funder

Ministry of Education, Culture, Sports, Science and Technology

Japan Science and Technology Agency

Publisher

American Physical Society (APS)

Subject

Physics and Astronomy (miscellaneous),General Materials Science

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevMaterials.7.093805/fulltext

Reference82 articles.

1. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

2. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

3. Machine-learning-assisted low dielectric constant polymer discovery

4. Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening

5. Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

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