Virtual experimentations by deep learning on tangible materials

Virtual experimentations by deep learning on tangible materials

Artificial intelligence may significantly accelerate the discovery of new materials but is not easily applicable to non-periodic structures. Here, a deep learning framework is proposed to predict properties of tangible carbon nanotubes by generating virtual structures at different scales and composi...

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Main Authors: Takashi Honda, Shun Muroga, Hideaki Nakajima, Taiyo Shimizu, Kazufumi Kobashi, Hiroshi Morita, Toshiya Okazaki, Kenji Hata
Format: Article
Language:English
Published: Nature Publishing Group 2021-08-01
Series:Communications Materials
Online Access:https://doi.org/10.1038/s43246-021-00195-2
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spelling doaj-1f287afbde7e4b7b8103d31f93afd8162021-09-05T11:25:11ZengNature Publishing GroupCommunications Materials2662-44432021-08-01211810.1038/s43246-021-00195-2Virtual experimentations by deep learning on tangible materialsTakashi Honda0Shun Muroga1Hideaki Nakajima2Taiyo Shimizu3Kazufumi Kobashi4Hiroshi Morita5Toshiya Okazaki6Kenji Hata7Research Association of High-Throughput Design and Development for Advanced Functional Materials (ADMAT)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology (AIST)Artificial intelligence may significantly accelerate the discovery of new materials but is not easily applicable to non-periodic structures. Here, a deep learning framework is proposed to predict properties of tangible carbon nanotubes by generating virtual structures at different scales and compositions.https://doi.org/10.1038/s43246-021-00195-2
collection DOAJ
language English
format Article
sources DOAJ
author Takashi Honda
Shun Muroga
Hideaki Nakajima
Taiyo Shimizu
Kazufumi Kobashi
Hiroshi Morita
Toshiya Okazaki
Kenji Hata
spellingShingle Takashi Honda
Shun Muroga
Hideaki Nakajima
Taiyo Shimizu
Kazufumi Kobashi
Hiroshi Morita
Toshiya Okazaki
Kenji Hata
Virtual experimentations by deep learning on tangible materials
Communications Materials
author_facet Takashi Honda
Shun Muroga
Hideaki Nakajima
Taiyo Shimizu
Kazufumi Kobashi
Hiroshi Morita
Toshiya Okazaki
Kenji Hata
author_sort Takashi Honda
title Virtual experimentations by deep learning on tangible materials
title_short Virtual experimentations by deep learning on tangible materials
title_full Virtual experimentations by deep learning on tangible materials
title_fullStr Virtual experimentations by deep learning on tangible materials
title_full_unstemmed Virtual experimentations by deep learning on tangible materials
title_sort virtual experimentations by deep learning on tangible materials
publisher Nature Publishing Group
series Communications Materials
issn 2662-4443
publishDate 2021-08-01
description Artificial intelligence may significantly accelerate the discovery of new materials but is not easily applicable to non-periodic structures. Here, a deep learning framework is proposed to predict properties of tangible carbon nanotubes by generating virtual structures at different scales and compositions.
url https://doi.org/10.1038/s43246-021-00195-2
work_keys_str_mv AT takashihonda virtualexperimentationsbydeeplearningontangiblematerials
AT shunmuroga virtualexperimentationsbydeeplearningontangiblematerials
AT hideakinakajima virtualexperimentationsbydeeplearningontangiblematerials
AT taiyoshimizu virtualexperimentationsbydeeplearningontangiblematerials
AT kazufumikobashi virtualexperimentationsbydeeplearningontangiblematerials
AT hiroshimorita virtualexperimentationsbydeeplearningontangiblematerials
AT toshiyaokazaki virtualexperimentationsbydeeplearningontangiblematerials
AT kenjihata virtualexperimentationsbydeeplearningontangiblematerials
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