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  1. 04 物質創成科学
  2. 01 学術雑誌論文

Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers

http://hdl.handle.net/10061/0002000467
http://hdl.handle.net/10061/0002000467
b7350977-4ecf-44c2-9307-e997e6c95f12
アイテムタイプ 学術雑誌論文 / Journal Article(1)
公開日 2024-06-13
タイトル
タイトル Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers
言語
言語 eng
資源タイプ
資源タイプ journal article
アクセス権
アクセス権 open access
著者 Takasuka, Shogo

× Takasuka, Shogo

en Takasuka, Shogo

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Oikawa, Shunto

× Oikawa, Shunto

en Oikawa, Shunto

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Yoshimura, Takayoshi

× Yoshimura, Takayoshi

en Yoshimura, Takayoshi

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Ito, Sho

× Ito, Sho

en Ito, Sho

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原嶋, 庸介

× 原嶋, 庸介

WEKO 46
e-Rad_Researcher 90748203

ja 原嶋, 庸介

ja-Kana ハラシマ, ヨウスケ

en Harashima, Yosuke

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高山, 大鑑

× 高山, 大鑑

WEKO 35597

ja 高山, 大鑑

ja-Kana タカヤマ, トモアキ

en Takayama, Tomoaki

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Asano, Shigehito

× Asano, Shigehito

en Asano, Shigehito

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Kurosawa, Akira

× Kurosawa, Akira

en Kurosawa, Akira

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Sugawara, Tetsunori

× Sugawara, Tetsunori

en Sugawara, Tetsunori

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Hatanaka, Miho

× Hatanaka, Miho

en Hatanaka, Miho

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宮尾, 知幸

× 宮尾, 知幸

WEKO 123
e-Rad_Researcher 20823909

ja 宮尾, 知幸

ja-Kana ミヤオ, トモユキ

en Miyao, Tomoyuki

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松原, 崇充

× 松原, 崇充

WEKO 181
e-Rad_Researcher 20508056

ja 松原, 崇充

ja-Kana マツバラ, タカミツ

en Matsubara, Takamitsu

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Ohnishi, Yu-ya

× Ohnishi, Yu-ya

en Ohnishi, Yu-ya

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網代, 広治

× 網代, 広治

WEKO 15
e-Rad_Researcher 50437331

ja 網代, 広治

ja-Kana アジロ, ヒロハル

en Ajiro, Hiroharu

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藤井, 幹也

× 藤井, 幹也

WEKO 35595

ja 藤井, 幹也

ja-Kana フジイ, ミキヤ

en Fujii, Mikiya

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抄録
内容記述タイプ Abstract
内容記述 The properties of polymers are highly dependent on the combination and composition ratio of the monomers used to prepare them; however, the large number of available monomers makes an exhaustive investigation of all the possible combinations difficult. In the present study, five binary copolymers were prepared by radical polymerization using a flow reactor and the prediction performance of a machine learning model constructed using the obtained data was evaluated for the interpolation and extrapolation regions. Copolymer analysis was performed using ultra-high-performance liquid chromatography, and the measurement results were analysed to calculate the monomer conversion and monomer composition ratio in the polymer, which were used as objective variables. A prediction model was constructed using the process variables during polymerization and additional molecular descriptors (i.e., molecular flags (one-hot encoding), fingerprints or quantum chemical calculation values) related to the monomer type as explanatory variables. In the interpolated regions where all monomer types used were included in the training data, the prediction accuracy was high irrespective of the molecular descriptors added to the process variables. In the extrapolation region, the model that included explanatory variables corresponding to quantum chemical calculation values representing the energy generated when radical reactions occur, showed a high prediction accuracy for each objective variable. We found that quantum chemical calculation values (especially the molecular orbital energy of monomers in the extrapolation region) are important factors in the search for new binary copolymers prepared by radical polymerization. The proposed model is expected to accelerate the development of polymers using new monomers.
書誌情報 en : Digital Discovery

巻 2, 号 3, p. 809-818, 発行日 2023-05-08
出版者
出版者 Royal Society of Chemistry
ISSN
収録物識別子タイプ EISSN
収録物識別子 2635-098X
出版者版DOI
関連タイプ isReplacedBy
識別子タイプ DOI
関連識別子 https://doi.org/10.1039/D2DD00144F
出版者版URI
関連タイプ isReplacedBy
識別子タイプ URI
関連識別子 https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00144f
権利
権利情報Resource http://creativecommons.org/licenses/by/3.0/
権利情報 $00A9 2023 The Author(s). This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
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出版タイプ NA
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