Kumagai Masaya 熊谷将也

Researcher Number	00881054
Other IDs
Affiliation (Current)	2025: 京都大学, 複合原子力科学研究所, 研究員
Affiliation (based on the past Project Information) *help	2020 – 2023: 京都大学, 複合原子力科学研究所, 特定助教
Review Section/Research Field	Principal Investigator Basic Section 26020:Inorganic materials and properties-related / 0401:Materials engineering, chemical engineering, and related fields
Keywords	Principal Investigator 材料工学 / 機械学習 / マテリアルズ・インフォマティクス / グラフ構造 / 結晶グラフ / 結晶構造 / 熱電変換材料 / マテリアルズインフォマティクス / プロセス・インフォマティクス

Research Projects
(2 results)
Research Products
(12 results)

大規模な実験的物性データを用いた結晶構造と物性との関係性解明Principal Investigator
- Principal Investigator
  
  熊谷将也
- Project Period (FY)
  2022 – 2024
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 26020:Inorganic materials and properties-related
- Research Institution
  Kyoto University
Development of Next-Generation Machine Intelligence for Predicting Material Properties, Considering the Influence of Experimental Processes and Sample StructuresPrincipal Investigator
- Principal Investigator
  
  Kumagai Masaya
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Research Activity Start-up
- Review Section
  
  0401:Materials engineering, chemical engineering, and related fields
- Research Institution
  Kyoto University

All 2023 2022 2021

All Journal Article Presentation

[Journal Article] DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials2023
- Author(s)
  Hirofumi Tsuruta, Yukari Katsura, Masaya Kumagai
- Journal Title
  
  IEEE ICMLA2023
  
  Volume: -
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-22K14474
[Journal Article] Effects of data bias on machine-learning?based material discovery using experimental property data2022
- Author(s)
  Kumagai Masaya、Ando Yuki、Tanaka Atsumi、Tsuda Koji、Katsura Yukari、Kurosaki Ken
- Journal Title
  
  Science and Technology of Advanced Materials: Methods
  
  Volume: 2 Issue: 1 Pages: 302-309
- DOI
  10.1080/27660400.2022.2109447
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K22466, KAKENHI-PLANNED-19H05820
[Journal Article] Design of Deep Learning Model for Predicting Material Properties Using Crystal Structure Represented by Three-Dimensional Mesh2022
- Author(s)
  鶴田博文、桂ゆかり、熊谷将也
- Journal Title
  
  Proceedings of the Annual Conference of JSAI
  
  Volume: JSAI2022 Issue: 0 Pages: 4C3GS1004-4C3GS1004
- DOI
  10.11517/pjsai.JSAI2022.0_4C3GS1004
- Language
  Japanese
- Data Source
  KAKENHI-PROJECT-22K14474
[Presentation] DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials2023
- Author(s)
  Hirofumi Tsuruta, Yukari Katsura, Masaya Kumagai
- Organizer
  IEEE ICMLA2023
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14474
[Presentation] Direct prediction of mechanical properties from X-ray diffraction patterns using machine learning2022
- Author(s)
  Naoki Hato, Masaya Kumagai, Ken Kurosaki
- Organizer
  TMS 2022 Annual Meeting & Exhibition
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K22466
[Presentation] マテリアルズ・インフォマティクス― 大規模な実験データ収集Webシステムの開発と応用 ―2022
- Author(s)
  熊谷将也
- Organizer
  複合原子力化学研究所第56回学術講演会
- Invited
- Data Source
  KAKENHI-PROJECT-20K22466
[Presentation] 3次元メッシュで表現した結晶構造を用いた材料物性の予測に向けた深層学習モデルの設計2022
- Author(s)
  鶴田博文, 桂ゆかり, 熊谷将也
- Organizer
  2022年度人工知能学会全国大会（第36回）
- Data Source
  KAKENHI-PROJECT-22K14474
[Presentation] Applicability domain for prediction models of thermoelectric properties based on similarity to known materials2022
- Author(s)
  Masaya Kumagai
- Organizer
  TMS2022
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K22466
[Presentation] 3次元メッシュで表現した結晶構造を用いた材料物性の予測に向けた深層学習モデルの設計2022
- Author(s)
  鶴田博文, 桂ゆかり, 熊谷将也
- Organizer
  2022年度人工知能学会全国大会（第36回）
- Data Source
  KAKENHI-PROJECT-20K22466
[Presentation] 実験MIと新材料探索2022
- Author(s)
  熊谷将也, 安藤有希, 田中敦美, 津田宏治, 桂ゆかり, 黒崎健
- Organizer
  第83回応用物理学会秋季学術講演会
- Invited
- Data Source
  KAKENHI-PROJECT-22K14474
[Presentation] Applicability domain for prediction models of thermoelectric properties based on similarity to known materials2022
- Author(s)
  Masaya Kumagai, Yukari Katsura, Yuki Ando, Atsumi Tanaka, Koji Tsuda, Ken Kurosaki
- Organizer
  TMS 2022 Annual Meeting & Exhibition
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K22466
[Presentation] 既知材料との類似性に基づいた熱電特性予測モデルの適用範囲2021
- Author(s)
  熊谷将也
- Organizer
  日本熱電学会
- Data Source
  KAKENHI-PROJECT-20K22466

Kumagai Masaya 熊谷 将也

Research Projects

Research Products

大規模な実験的物性データを用いた結晶構造と物性との関係性解明Principal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development of Next-Generation Machine Intelligence for Predicting Material Properties, Considering the Influence of Experimental Processes and Sample StructuresPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials2023

Author(s)

Journal Title

Data Source

[Journal Article] Effects of data bias on machine-learning?based material discovery using experimental property data2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Design of Deep Learning Model for Predicting Material Properties Using Crystal Structure Represented by Three-Dimensional Mesh2022

Author(s)

Journal Title

DOI

Language

Data Source

[Presentation] DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials2023

Author(s)

Organizer

Data Source

[Presentation] Direct prediction of mechanical properties from X-ray diffraction patterns using machine learning2022

Author(s)

Organizer

Data Source

[Presentation] マテリアルズ・インフォマティクス― 大規模な実験データ収集Webシステムの開発と応用 ―2022

Author(s)

Organizer

Data Source

[Presentation] 3次元メッシュで表現した結晶構造を用いた材料物性の予測に向けた深層学習モデルの設計2022

Author(s)

Organizer

Data Source

[Presentation] Applicability domain for prediction models of thermoelectric properties based on similarity to known materials2022

Author(s)

Organizer

Data Source

[Presentation] 3次元メッシュで表現した結晶構造を用いた材料物性の予測に向けた深層学習モデルの設計2022

Author(s)

Organizer

Data Source

[Presentation] 実験MIと新材料探索2022

Author(s)

Organizer

Data Source

[Presentation] Applicability domain for prediction models of thermoelectric properties based on similarity to known materials2022

Author(s)

Organizer

Data Source

[Presentation] 既知材料との類似性に基づいた熱電特性予測モデルの適用範囲2021

Author(s)

Organizer

Data Source

Kumagai Masaya 熊谷将也