TERAMAE Hiroyuki

写真a

Affiliation

Faculty of Science Depertment of Chemistry

Title

Professor

External Link

Degree 【 display / non-display

  • Ph.D ( 1984.03   Kyoto University )

Research Areas 【 display / non-display

  • Nanotechnology/Materials / Fundamental physical chemistry

From School 【 display / non-display

  • Kyoto University   Faculty of Engineering   Graduated

    - 1979.03

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    Country:Japan

From Graduate School 【 display / non-display

  • Kyoto University   Graduate School, Division of Engineering   Doctor's Course   Completed

    - 1984.03

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    Country:Japan

  • Kyoto University   Graduate School, Division of Engineering   Master's Course   Completed

    - 1981.03

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    Country:Japan

Employment Record in Research 【 display / non-display

  • Josai University   Faculty of Science   Depertment of Chemistry   Professor

    2004.04

External Career 【 display / non-display

  • Meiji University   Lecturer

    2009.04 - 2023.03

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    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

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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

 

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  -   

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

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    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

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    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

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    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

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    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

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    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

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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

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    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

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    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 

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    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 

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    Language:Japanese   Book type:Scholarly book

Misc 【 display / non-display

  • Relation Between Machine Learning and Chemistry Invited

    2022.03

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    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

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    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

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (international conference proceedings)   Publisher:(石油学会)  

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

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    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

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    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

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    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 

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    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

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    Event date: 2023.06

    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:東京   Country:Japan  

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Other research activities 【 display / non-display

  • Editorial board of Journal of Chemistry

    2015.01 - 2018.08

 

Teaching Experience 【 display / non-display

  • 分子物理学

  • 化学情報処理