Tsubaki Masashi 椿真史

Researcher Number	80803874
Affiliation (Current)	2025: 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員
Affiliation (based on the past Project Information) *help	2023 – 2025: 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 2020 – 2022: 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 2017 – 2018: 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員
Review Section/Research Field	Principal Investigator Basic Section 61030:Intelligent informatics-related / Life / Health / Medical informatics Except Principal Investigator Basic Section 35030:Organic functional materials-related / Transformative Research Areas, Section (II) / Basic Section 33020:Synthetic organic chemistry-related
Keywords	Principal Investigator 機械学習 / 深層学習 / 第一原理計算 / マテリアルズ・インフォマティクス / データ駆動科学 / 密度汎関数法 / 量子化学計算 / 転移学習 / 密度汎関数理論 / 人工知能 / 創薬 … More Except Principal Investigator … More 機械学習 / 有機合成 / 物性予測 / 発光ダイナミクス / 発光特性 / 有機分子 / 予測モデル / 逆解析 / 触媒設計 / 収率予測 / 触媒反応 / 触媒 / 転移学習 Less

Development of a predictive model for the emission characteristics of thermally activated delayed fluorescence materials based on emission dynamics
- Principal Investigator
  
  細貝拓也
- Project Period (FY)
  2025 – 2027
- Research Category
  
  Grant-in-Aid for Scientific Research (B)
- Review Section
  
  Basic Section 35030:Organic functional materials-related
- Research Institution
  National Institute of Advanced Industrial Science and Technology
Development of automated catalyst design methodology based on inversed analysis of predictive model
- Principal Investigator
  
  矢田陽
- Project Period (FY)
  2021 – 2025
- Research Category
  
  Grant-in-Aid for Transformative Research Areas (A)
- Review Section
  
  Transformative Research Areas, Section (II)
- Research Institution
  National Institute of Advanced Industrial Science and Technology
Development and validation of quantum physics and chemistry-interpretable deep learning methods in data-driven sciencePrincipal Investigator
- Principal Investigator
  
  Tsubaki Masashi
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 61030:Intelligent informatics-related
- Research Institution
  National Institute of Advanced Industrial Science and Technology
Data-Driven Descriptor Generation and its Application to Prediction of Organic Synthetic and Catalytic Reactions
- Principal Investigator
  
  Yada Akira
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Scientific Research (B)
- Review Section
  
  Basic Section 33020:Synthetic organic chemistry-related
- Research Institution
  National Institute of Advanced Industrial Science and Technology
Deep representation learning for drugs and proteins with neural networksPrincipal Investigator
- Principal Investigator
  
  Tsubaki Masashi
- Project Period (FY)
  2017 – 2018
- Research Category
  
  Grant-in-Aid for Research Activity Start-up
- Research Field
  
  Life / Health / Medical informatics
- Research Institution
  National Institute of Advanced Industrial Science and Technology

All 2024 2023 2021 2020 2019 2018 2017

All Journal Article Presentation Book Patent

[Book] 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～組成予測や化学構造の生成、合成経路探索や反応条件最適化、毒性評価～2023
- Author(s)
  椿真史、その他
- Total Pages
  657
- Publisher
  技術情報協会
- ISBN
  9784861049446
- Data Source
  KAKENHI-PROJECT-20K19876
[Journal Article] Quantum deep descriptor: Physically informed transfer learning from small molecules to polymers2021
- Author(s)
  Masashi Tsubaki and Teruyasu Mizoguchi
- Journal Title
  
  Journal of Chemical Theory and Computation
  
  Volume: 17 Issue: 12 Pages: 7814-7821
- DOI
  10.1021/acs.jctc.1c00568
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K19876
[Journal Article] Learning excited states from ground states by using an artificial neural network2020
- Author(s)
  Kiyohara Shin、Tsubaki Masashi、Mizoguchi Teruyasu
- Journal Title
  
  npj Computational Materials
  
  Volume: 6 Issue: 1 Pages: 1-6
- DOI
  10.1038/s41524-020-0336-3
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PLANNED-19H05787, KAKENHI-PROJECT-20H02747, KAKENHI-PROJECT-20J00773, KAKENHI-PROJECT-19H00818
[Journal Article] Quantum Deep Field: Data-Driven Wave Function, Electron Density Generation, and Atomization Energy Prediction and Extrapolation with Machine Learning2020
- Author(s)
  Masashi Tsubaki and Teruyasu Mizoguchi
- Journal Title
  
  Physical Review Letters
  
  Volume: 125 Issue: 20 Pages: 206401-206401
- DOI
  10.1103/physrevlett.125.206401
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K19876, KAKENHI-PLANNED-19H05787, KAKENHI-PROJECT-20H02747
[Journal Article] On the equivalence of molecular graph convolution and molecular wave function with poor basis set2020
- Author(s)
  Masashi Tsubaki、Teruyasu Mizoguchi
- Journal Title
  
  Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
  
  Volume: 33 Pages: 1982-1993
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20H02747
[Journal Article] On the equivalence of molecular graph convolution and molecular wave function with poor basis set2020
- Author(s)
  Masashi Tsubaki and Teruyasu Mizoguchi
- Journal Title
  
  Advances in Neural Information Processing Systems
  
  Volume: 33
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K19876
[Journal Article] Quantitative estimation of properties from core-loss spectrum via neural network2019
- Author(s)
  Shin Kiyohara, Masashi Tsubaki, Kunyen Liao, and Teruyasu Mizoguchi
- Journal Title
  
  Journal of Physics: Materials
  
  Volume: 1 Pages: 1-2
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-17H07392
[Journal Article] Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks2018
- Author(s)
  Masashi Tsubaki and Teruyasu Mizoguchi
- Journal Title
  
  The Journal of Physical Chemistry Letters
  
  Volume: 9 Pages: 5733-5741
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-17H07392
[Journal Article] Mean-field theory of Graph Neural Networks in Graph Partitioning2018
- Author(s)
  Tatsuro Kawamoto, Masashi Tsubaki, and Tomoyuki Obuchi
- Journal Title
  
  Advances in Neural Information Processing Systems
  
  Volume: 1 Pages: 1-2
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-17H07392
[Journal Article] Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences2018
- Author(s)
  Masashi Tsubaki, Kentaro Tomii, and Jun Sese
- Journal Title
  
  Bioinformatics
  
  Volume: 35 Pages: 309-318
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-17H07392
[Patent] 物性予測方法及び物性予測装置2024
- Inventor(s)
  椿真史
- Industrial Property Rights Holder
  国立研究開発法人産業技術総合研究所
- Industrial Property Rights Type
  特許
- Acquisition Date
  2024
- Data Source
  KAKENHI-PROJECT-20K19876
[Patent] 物性予測方法及び物性予測装置2020
- Inventor(s)
  椿真史
- Industrial Property Rights Holder
  国立研究開発法人産業技術総合研究所
- Industrial Property Rights Type
  特許
- Filing Date
  2020
- Data Source
  KAKENHI-PROJECT-20K19876
[Presentation] 深層学習に基づく波動関数・電子構造の記述子表現と転移学習への応用2021
- Author(s)
  椿真史
- Organizer
  日本化学会春季年会 2021
- Invited
- Data Source
  KAKENHI-PROJECT-20K19876
[Presentation] 深層学習に基づく波動関数・電子構造の記述子表現と転移学習への応用依頼講演2021
- Author(s)
  椿真史
- Organizer
  日本化学会第101回春季大会
- Invited
- Data Source
  KAKENHI-PROJECT-20K19876
[Presentation] 量子化学計算のための深層学習技術の基礎と応用2021
- Author(s)
  椿真史
- Organizer
  顕微鏡計測インフォマティックス研究部会
- Invited
- Data Source
  KAKENHI-PROJECT-20K19876
[Presentation] 深層学習に基づく波動関数・電子構造の記述子表現と転移学習への応用2021
- Author(s)
  椿　真史
- Organizer
  日本化学会第101春季年会 (2021)
- Invited
- Data Source
  KAKENHI-PROJECT-20H02747
[Presentation] 創薬と新材料開発のための人工知能2021
- Author(s)
  椿　真史
- Organizer
  情報処理学会全国大会 2021
- Invited
- Data Source
  KAKENHI-PROJECT-20H02747
[Presentation] 創薬と新材料開発のための人工知能2021
- Author(s)
  椿真史
- Organizer
  情報処理学会全国大会 2021
- Invited
- Data Source
  KAKENHI-PROJECT-20K19876
[Presentation] 深層学習を用いた化合物とタンパク質の相互作用予測2018
- Author(s)
  椿真史
- Organizer
  創薬インフォマティクス研究会
- Invited
- Data Source
  KAKENHI-PROJECT-17H07392
[Presentation] End-to-end Learning of Graph Neural Networks for Latent Molecular Representations2017
- Author(s)
  Masashi Tsubaki
- Organizer
  Advances in Neural Information Processing Systems (NIPS 2017) Workshop, Machine Learning for Molecules and Materials,
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-17H07392

1. Yada Akira (70619965)

# of Collaborated Projects: 2 results

# of Collaborated Products: 0 results
2. 細貝拓也 (90613513)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
3. 畠山琢次 (90432319)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
4. 中野谷一 (90633412)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
5. アルブレヒト建 (50599561)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
6. Asho Hideki

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
7. Kanemura Atsunori

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
8. 溝口照康

# of Collaborated Projects: 0 results

# of Collaborated Products: 1 results

Tsubaki Masashi 椿 真史

Research Projects

Research Products

Co-Researchers

Development of a predictive model for the emission characteristics of thermally activated delayed fluorescence materials based on emission dynamics

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development of automated catalyst design methodology based on inversed analysis of predictive model

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development and validation of quantum physics and chemistry-interpretable deep learning methods in data-driven sciencePrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Data-Driven Descriptor Generation and its Application to Prediction of Organic Synthetic and Catalytic Reactions

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Deep representation learning for drugs and proteins with neural networksPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Research Field

Research Institution

[Book] 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～組成予測や化学構造の生成、合成経路探索や反応条件最適化、毒性評価～2023

Author(s)

Total Pages

Publisher

ISBN

Data Source

[Journal Article] Quantum deep descriptor: Physically informed transfer learning from small molecules to polymers2021

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Learning excited states from ground states by using an artificial neural network2020

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Quantum Deep Field: Data-Driven Wave Function, Electron Density Generation, and Atomization Energy Prediction and Extrapolation with Machine Learning2020

Author(s)

Journal Title

DOI

Data Source

[Journal Article] On the equivalence of molecular graph convolution and molecular wave function with poor basis set2020

Author(s)

Journal Title

Data Source

[Journal Article] On the equivalence of molecular graph convolution and molecular wave function with poor basis set2020

Author(s)

Journal Title

Data Source

[Journal Article] Quantitative estimation of properties from core-loss spectrum via neural network2019

Author(s)

Journal Title

Data Source

[Journal Article] Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks2018

Author(s)

Journal Title

Data Source

[Journal Article] Mean-field theory of Graph Neural Networks in Graph Partitioning2018

Author(s)

Journal Title

Data Source

[Journal Article] Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences2018

Author(s)

Journal Title

Data Source

[Patent] 物性予測方法及び物性予測装置2024

Tsubaki Masashi 椿真史