ハワイアンドリームクリスマス

Researchers Details of a Researcher Home Japanese このページはJavascriptを使用しています。すべての機能を使用するためにはJavascript を有効にする必要があります。 　 Personnel Information 　 Profile 　 Degree 　 Research Areas 　 From School 　 From Graduate School 　 Employment Record in Research 　 External Career 　 Professional Memberships 　 Research Activity 　 Research Career 　 Papers 　 Books and Other Publications 　 Misc 　 Presentations 　 Other research activities 　 Education Activity 　 Teaching Experience Updated on 2024/03/26 TERAMAE Hiroyuki Affiliation Faculty of Science Depertment of Chemistry Title Professor External Link To the head of this page.▲ Degree 【 display / non-display 】 To the head of this page.▲ Degree 【 display / non-display 】 Ph.D ( 1984.03   Kyoto University ) To the head of this page.▲ Research Areas 【 display / non-display 】 To the head of this page.▲ Research Areas 【 display / non-display 】 Nanotechnology/Materials / Fundamental physical chemistry To the head of this page.▲ From School 【 display / non-display 】 To the head of this page.▲ From School 【 display / non-display 】 Kyoto University   Faculty of Engineering   Graduated - 1979.03 　 More details Country：Japan To the head of this page.▲ From Graduate School 【 display / non-display 】 To the head of this page.▲ From Graduate School 【 display / non-display 】 Kyoto University   Graduate School, Division of Engineering   Doctor&＃39;s Course   Completed - 1984.03 　 More details Country：Japan Kyoto University   Graduate School, Division of Engineering   Master&＃39;s Course   Completed - 1981.03 　 More details Country：Japan To the head of this page.▲ Employment Record in Research 【 display / non-display 】 To the head of this page.▲ Employment Record in Research 【 display / non-display 】 Josai University   Faculty of Science   Depertment of Chemistry   Professor 2004.04 To the head of this page.▲ External Career 【 display / non-display 】 To the head of this page.▲ External Career 【 display / non-display 】 Meiji University   Lecturer 2009.04 - 2023.03 　 More details Country：Japan Rikkyo University   Lecturer 2002.04 - 2006.03 ATR適応コミュニケーション研究所   主任研究員 2001.10 - 2002.03 ATR環境適応通信研究所   主任研究員 1999.01 - 2001.09 Rikkyo University   Lecturer 1997.04 - 1998.03 display all >> To the head of this page.▲ Professional Memberships 【 display / non-display 】 To the head of this page.▲ Professional Memberships 【 display / non-display 】 日本コンピュータ化学会 1998.04 アメリカ化学会 1996.04 応用物理学会 1992.07 - 1996.03 日本化学会 1990.04 To the head of this page.▲   Research Career 【 display / non-display 】 To the head of this page.▲ Research Career 【 display / non-display 】 分子軌道法と機械学習による分子物性の予測 The Other Research Programs   Project Year： 2017.04  -    分子軌道法を用いたナノマテリアル内での化学反応に関する研究 Funded Research   Project Year： 2008.12  -  2014.03  分子内プロトン移動反応に関する理論的研究 Cooperative Research   Project Year： 2008.04  -  2013.03  高次元アルゴリズムによる分子構造最適化の研究 The Other Research Programs   Project Year： 2004.04  -    To the head of this page.▲ Papers 【 display / non-display 】 To the head of this page.▲ Papers 【 display / non-display 】 Prediction of log P Parameter Using Molecular Orbital Energies and Machine Learning Invited Reviewed Hiroyuki Teramae 22 ( 2 )   34 - 36   2024.02 　More details Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   DOI： https://doi.org/10.2477/jccj.2023-0001 Prediction of Entropy by Machine Learning with Molecular Orbital Energies Invited Reviewed Takafumi Yuuki, Wakana Nakahara, Hiroyuki Teramae 22 ( 2 )   31 - 33   2024.02 　More details Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   DOI： https://doi.org/10.2477/jccj.2023-0001 Machine Learning Study of Antioxidant Effects with Molecular Orbital Energies as Explanatory Variables Invited Reviewed Journal of Computational Chemistry, Japan   21 ( 4 )   103 - 105   2023.04 　More details Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   DOI： https://doi.org/10.2477/jccj.2023-0001 Prediction of molecular properties with machine learning and molecular orbital energies Invited Reviewed Hiroyuki Teramae, Meiyan Xuan, Jun Takayama, Mari Okazaki and Takeshi Sakamoto AIP Conference Proceedints   2611   02007   2022.11 　More details Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   DOI： https://doi.org/10.1063/5.0119589 Possible Prediction of Molecular Properties with Machine Learning and Molecular Orbital Energies Invited Reviewed Hiroyuki Teramae, Xuan Meiyan, Tsukasa Yamashita, Jun Takayama, Mari Okazaki, Takeshi Sakamoto Proceedings of International Symposium on Environmental-Life Science and Nanoscales Technology 2019   XVII - XXI   2020.09 　More details Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：University of Yangon   The ferulic acid is known to have strong antioxidant properties. In the present study, we have investigated the electronic structures of the ferulic acid and its radical extracting the hydrogen atom from its phenolic hydroxyl group. We have discussed the relation of the results with the radical scavenging activity with the DPPH reagent, IC50, measured by Sakamoto et al. by several machine learning models. We use Gaussian16 program package to calculate the optimized geometries and the molecular orbitals of FA and its derivatives at RHF/6-31G** level and the radicals of FA and its derivatives which are made by removing the hydrogen atom from the phenolic hydroxyl group. The machine learning is performed with the R/caret packages. We use the orbital energy levels of the radical forms of SOMO, SOMO-1, SOMO, LUMO, and LUMO, the neutral forms of HOMO-1, HOMO, LUMO, and LUMO+1, and the energy difference between the radical and neutral forms as the explanatory variables. We make the machine learning with these ten explanatory variables and IC50 value as the explained variable. For the regression method, we use partial least square, random forest, neural network, and krlsRadial. All the methods give moderate/strong correlation coefficients and there should be a strong correlation. Furthermore, when we examine the machine learning with only the orbital energy levels of the radical forms, the correlation coefficients are almost the same. In conclusion, we confirm the IC50 values of the ferulic acid can be predicted by just molecular orbital energies display all >> To the head of this page.▲ Books and Other Publications 【 display / non-display 】 To the head of this page.▲ Books and Other Publications 【 display / non-display 】 ケモインフォマティクスにおける データ収集の最適化と解析手法 寺前裕之（ Role： Contributor ,  第4章第1節ケモインフォマティクスにおける機械学習モデルの種類と具体的活用法） 技術情報協会  2023.04  （ ISBN:978-4-86104-944-6 ） 　More details Total pages：657   Responsible for pages：209-217   Language：Japanese   Book type：Scholarly book Materials Informatics Questions and Answers Masahiro Kaneko, Kimito Funatsu, Hiroyuki Teramae etc.（ Role： Joint author ,  Chapter 8, Section 3, Question 4） JOHOKIKO CO. LTD.  2020.12  （ ISBN:978-4-86502-204-9 ） 　More details Total pages：597   Responsible for pages：516-518   Language：Japanese   Book type：Scholarly book A number of questions that arise during the introduction and operation of materials informatics are specifically resolved in a Q & A format. 導電性材料をめぐる最近の動向 寺前裕之（ Role： Sole author） 材料技術研究協会  1992.04  　More details Language：Japanese   Book type：Scholarly book ポリアセチレンの電子構造 山邊時雄，寺前裕之（ Role： Sole author ,  主要部分の執筆） 化学同人  1985.04  　More details Language：Japanese   Book type：Scholarly book To the head of this page.▲ Misc 【 display / non-display 】 To the head of this page.▲ Misc 【 display / non-display 】 Relation Between Machine Learning and Chemistry Invited 2022.03 　More details Language：Japanese   PubChem のデータを用いた分子座標の作成‐データベースを用いてGaussian16 の入力ファイルを作成する方法 ‐ 寺前裕之 城西情報科学研究   29   15 - 26   2022.03 　More details Authorship：Lead author,　Last author   Language：Japanese   Publishing type：Rapid communication, short report, research note, etc. (bulletin of university, research institution)   計算化学汎用プログラム 分子設計統合ソフト HyperChem 寺前裕之 PETROTECH   30 ( 5 )   346 - 350   2007.01 　More details Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)   Publisher：（石油学会）   To the head of this page.▲ Presentations 【 display / non-display 】 To the head of this page.▲ Presentations 【 display / non-display 】 分子軌道エネルギーによる構造活性相関法の開発 寺前 裕之, 藤堂 浩明 日本コンピュータ化学会2023年秋季年会  2023.11  日本コンピュータ化学会 　More details Event date： 2023.11 Language：Japanese   Presentation type：Oral presentation (general)   Venue：東京   Country：Japan   機械学習による分子軌道エネルギーのみを説明変数としたエントロピーの予測 結城敬史、寺前裕之 日本コンピュータ化学会2023年秋季年会  2023.11  日本コンピュータ化学会 　More details Event date： 2023.11 Language：Japanese   Presentation type：Poster presentation   Venue：東京   Country：Japan   分子軌道エネルギーの機械学習による構造活性相関 寺前 裕之, 藤堂 浩明 第46回ケモインフォマティクス討論会  2023.11  日本化学会ケモインフォマティクス部会 　More details Event date： 2023.11 Language：Japanese   Presentation type：Oral presentation (general)   Venue：東京   Country：Japan   分子軌道エネルギーを用いた機械学習によるlogPの予測 寺前裕之 分子科学討論会2023  2023.09  　More details Event date： 2023.09 Language：Japanese   Presentation type：Oral presentation (general)   Venue：大阪   Country：Japan   分子軌道エネルギーを用いた機械学習によるlogPの予測 寺前裕之 日本コンピュータ化学会2023年春季年会  2023.06  日本コンピュータ化学会 　More details Event date： 2023.06 Language：Japanese   Presentation type：Oral presentation (general)   Venue：東京   Country：Japan   display all >> To the head of this page.▲ Other research activities 【 display / non-display 】 To the head of this page.▲ Other research activities 【 display / non-display 】 Editorial board of Journal of Chemistry 2015.01 - 2018.08 To the head of this page.▲   Teaching Experience 【 display / non-display 】 To the head of this page.▲ Teaching Experience 【 display / non-display 】 分子物理学 化学情報処理 To the head of this page.▲   Copyright © Josai University, All Rights Reserved.