YOKOI Tatsuya 横井達矢

… Alternative Names

Yokoi Tatsuya 横井達矢

Less

Researcher Number	70791581
Other IDs
Affiliation (Current)	2025: 名古屋大学, 工学研究科, 准教授
Affiliation (based on the past Project Information) *help	2021 – 2024: 名古屋大学, 工学研究科, 講師
Review Section/Research Field	Principal Investigator Basic Section 26020:Inorganic materials and properties-related / Science and Engineering Except Principal Investigator Basic Section 26010:Metallic material properties-related
Keywords	Principal Investigator 機械学習型原子間ポテンシャル / 第一原理計算 / 機械学習原子間ポテンシャル / 原子間ポテンシャル / 機械学習 / 粒界 / 半導体転位 / 半導体 / 転位 / 自由エネルギー計算 … More / 圧縮センシング / セラミックス粒界 / 自由エネルギー / 結晶粒界 / 熱力学 … More Except Principal Investigator 界面 / 粒界 / 分子動力学法 / 格子欠陥 / 第一原理計算 / 電子伝導 / 熱伝導 / 粒界・界面 / 転位 Less

Backcasting Materials Design through Uncovering Mechanisms of Electronic and Thermal Conduction by Control Dislocation and Grain boundaries
- Principal Investigator
  
  吉矢真人
- Project Period (FY)
  2023 – 2026
- Research Category
  
  Grant-in-Aid for Scientific Research (B)
- Review Section
  
  Basic Section 26010:Metallic material properties-related
- Research Institution
  Osaka University
Machine-learning descriptor and interatomic potential for understanding interaction between general grain boundaries and other lattice defectsPrincipal Investigator
- Principal Investigator
  
  横井達矢
- Project Period (FY)
  2023 – 2025
- Research Category
  
  Grant-in-Aid for Scientific Research (C)
- Review Section
  
  Basic Section 26020:Inorganic materials and properties-related
- Research Institution
  Nagoya University
Theoretical analysis of dislocation-core structure and its dynamics in semiconductorsPrincipal Investigator
- Principal Investigator
  
  横井達矢
- Project Period (FY)
  2022 – 2023
- Research Category
  
  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
- Review Section
  
  Science and Engineering
- Research Institution
  Nagoya University
First-principles thermodynamics for optimal design of atomic structure and properties of grain boundaries in ceramic materialsPrincipal Investigator
- Principal Investigator
  
  Yokoi Tatsuya
- Project Period (FY)
  2021 – 2022
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 26020:Inorganic materials and properties-related
- Research Institution
  Nagoya University

All 2023 2022 2021

All Journal Article Presentation

[Journal Article] Anharmonicity in grain boundary energy for Al: Thermodynamic integration with artificial-neural-network potential2023
- Author(s)
  M. Matsuura, T. Yokoi, Y. Ogura, K. Matsunaga
- Journal Title
  
  Scr. Mater.
  
  Volume: 236 Pages: 115685-115685
- DOI
  10.1016/j.scriptamat.2023.115685
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-23K04381, KAKENHI-PUBLICLY-22H04508
[Journal Article] Atomic structures of grain boundaries for Si and Ge: A simulated annealing method with artificial-neural-network interatomic potentials2023
- Author(s)
  T. Yokoi, Y. Oshima, K. Matsunaga
- Journal Title
  
  Journal of Physics and Chemistry of Solids
  
  Volume: 173 Pages: 111114-111114
- DOI
  10.1016/j.jpcs.2022.111114
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K14405, KAKENHI-PUBLICLY-22H04508
[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: its ability to predict the atomic structures, energetics and lattice vibrational properties for Al2023
- Author(s)
  T. Yokoi, M. Matsuura, Y. Oshima, K. Matsunaga
- Journal Title
  
  Physical Review Materials
  
  Volume: -
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K14405
[Journal Article] Electronic and atomic structures of Shockley-partial dislocations in CdX (X = S, Se and Te)2023
- Author(s)
  Hoshino Sena、Yokoi Tatsuya、Ogura Yu、Matsunaga Katsuyuki
- Journal Title
  
  J. Ceram. Soc. Japan
  
  Volume: 131 Issue: 10 Pages: 613-620
- DOI
  10.2109/jcersj2.23055
- ISSN
  1348-6535, 1882-0743
- Year and Date
  2023-10-01
- Language
  English
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-23KJ1059, KAKENHI-PUBLICLY-22H04508, KAKENHI-PROJECT-21H04618
[Journal Article] Grain boundary segregation of Y and Hf dopants in α-Al<sub>2</sub>O<sub>3</sub>: A Monte Carlo simulation with artificial-neural-network potential and density-functional-theory calculation2023
- Author(s)
  T. Yokoi, A. Hamajima, Y. Ogura, K. Matsunaga
- Journal Title
  
  J. Ceram. Soc. Japan
  
  Volume: 131 Issue: 10 Pages: 751-761
- DOI
  10.2109/jcersj2.23044
- ISSN
  1348-6535, 1882-0743
- Year and Date
  2023-10-01
- Language
  English
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Journal Article] Atomic and electronic structure of grain boundaries in a-Al2O3: A combination of machine learning, first-principles calculation and electron microscopy2023
- Author(s)
  Yokoi T.、Hamajima A.、Wei J.、Feng B.、Oshima Y.、Matsunaga K.、Shibata N.、Ikuhara Y.
- Journal Title
  
  Scripta Materialia
  
  Volume: 229 Pages: 115368-115368
- DOI
  10.1016/j.scriptamat.2023.115368
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-22K14463, KAKENHI-PROJECT-22H04960, KAKENHI-PROJECT-21K14405, KAKENHI-PLANNED-19H05788, KAKENHI-PUBLICLY-22H04508, KAKENHI-PROJECT-20H05659
[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: its ability to predict the atomic structures, energetics and lattice vibrational properties for Al2023
- Author(s)
  T. Yokoi, M. Matsuura, Y. Oshima, K. Matsunaga
- Journal Title
  
  Physical Review Materials
  
  Volume: -
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: Its ability to predict the atomic structures, energetics, and lattice vibrational properties for Al2023
- Author(s)
  T. Yokoi, M. Matsuura, Y. Oshima, K. Matsunaga
- Journal Title
  
  Phys. Rev. Mater.
  
  Volume: 7 Issue: 5 Pages: 053803-053803
- DOI
  10.1103/physrevmaterials.7.053803
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Journal Article] Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach2022
- Author(s)
  T. Yokoi, K. Adachi, S. Iwase, K. Matsunaga
- Journal Title
  
  Physical Chemistry Chemical Physics
  
  Volume: 24 Issue: 3 Pages: 1620-1629
- DOI
  10.1039/d1cp04329c
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K14405
[Journal Article] Atomic structures and stability of finite-size extended interstitial defects in silicon: Large-scale molecular simulations with a neural-network potential2022
- Author(s)
  M. Ohbitsu, T. Yokoi, Y. Noda, E. Kamiyama, T. Ushiro, H. Nagakura, K. Sueoka, K. Matsunaga
- Journal Title
  
  Scripta Materialia
  
  Volume: 214 Pages: 114650-114650
- DOI
  10.1016/j.scriptamat.2022.114650
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K14405
[Journal Article] Preferential Growth Mode of Large-Sized Vacancy Clusters in Silicon: A Neural-Network Potential and First-Principles Study2021
- Author(s)
  T. Ushiro, T. Yokoi, Y. Noda, E. Kamiyama, M. Ohbitsu, H. Nagakura, K. Sueoka, K. Matsunaga
- Journal Title
  
  Journal of Physical Chemistry C
  
  Volume: 125 Issue: 48 Pages: 26869-26882
- DOI
  10.1021/acs.jpcc.1c07973
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K14405
[Presentation] 格子欠陥特性の高精度予測に向けた機械学習型記述子・原子間ポテンシャルの構築2023
- Author(s)
  横井達矢
- Organizer
  第33回日本MRS年次大会
- Invited
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] 格子欠陥特性の高精度予測に向けた機械学習記述子および原子間ポテンシャルの構築2023
- Author(s)
  横井達矢、内田匡美、小椋優、松永克志
- Organizer
  日本金属学会 2023年秋期（第173回）講演大会
- Invited
- Data Source
  KAKENHI-PROJECT-23K04381
[Presentation] 格子欠陥特性の高精度予測に向けた機械学習記述子および原子間ポテンシャルの構築2023
- Author(s)
  横井達矢、内田匡美、小椋優、松永克志
- Organizer
  日本金属学会 2023年秋期（第173回）講演大会
- Invited
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] 格子欠陥特性の高精度予測に向けた機械学習型記述子・原子間ポテンシャルの構築2023
- Author(s)
  横井達矢
- Organizer
  第33回日本MRS年次大会
- Invited
- Data Source
  KAKENHI-PROJECT-23K04381
[Presentation] Artificial-neural-network potential for accurately predicting atomic structure and physical properties of lattice defects in semiconductors2022
- Author(s)
  T. Yokoi
- Organizer
  The 8th International Symposium on Advanced Science and Technology of Silicon Materials
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] Artificial-neural-network descriptor and interatomic potential for molecular simulations of lattice defects2022
- Author(s)
  T. Yokoi
- Organizer
  6th International Symposium on Frontier in Materials Science
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] 格子欠陥の原子構造・特性の予測に向けたニューラルネットワーク記述子および原子間ポテンシャルの構築2022
- Author(s)
  横井達矢、大島優、松永克志
- Organizer
  日本金属学会2022年秋期第171回講演大会
- Data Source
  KAKENHI-PROJECT-21K14405
[Presentation] 格子欠陥の原子構造と特性の高精度予測に向けたニューラルネットワーク記述子・原子間ポテンシャルの構築2022
- Author(s)
  横井達矢、大島優、松永克志
- Organizer
  第32回日本MRS年次大会
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] Artificial-neural-network potential for accurately predicting atomic structure and physical properties of lattice defects in semiconductors2022
- Author(s)
  T. Yokoi
- Organizer
  The 8th International Symposium on Advanced Science and Technology of Silicon Materials
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K14405
[Presentation] Artificial-neural-network descriptor and interatomic potential for molecular simulations of lattice defects2022
- Author(s)
  T. Yokoi
- Organizer
  6th International Symposium on Frontier in Materials Science
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K14405
[Presentation] Grain boundary structures and energetics in CdTe: An artificial-neural-network interatomic potential and first-principles approach2022
- Author(s)
  T. Yokoi, K. Adachi, Y. Oshima1, K. Matsunaga
- Organizer
  The 33rd International Photovoltaic Science and Engineering Conference
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K14405
[Presentation] 格子欠陥の原子構造・特性の予測に向けたニューラルネットワーク記述子および原子間ポテンシャルの構築2022
- Author(s)
  横井達矢、大島優、松永克志
- Organizer
  日本金属学会2022年秋期第171回講演大会
- Data Source
  KAKENHI-PUBLICLY-22H04508
[Presentation] Grain boundary structures and energetics in CdTe: An artificial-neural-network interatomic potential and first-principles approach2022
- Author(s)
  T. Yokoi, K. Adachi, Y. Oshima, K. Matsunaga
- Organizer
  The 33rd International Photovoltaic Science and Engineering Conference
- Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-22H04508

1. 吉矢真人 (00399601)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
2. 馮斌

# of Collaborated Projects: 0 results

# of Collaborated Products: 1 results

YOKOI Tatsuya 横井 達矢

Yokoi Tatsuya 横井 達矢

Research Projects

Research Products

Co-Researchers

Backcasting Materials Design through Uncovering Mechanisms of Electronic and Thermal Conduction by Control Dislocation and Grain boundaries

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Machine-learning descriptor and interatomic potential for understanding interaction between general grain boundaries and other lattice defectsPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Theoretical analysis of dislocation-core structure and its dynamics in semiconductorsPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

First-principles thermodynamics for optimal design of atomic structure and properties of grain boundaries in ceramic materialsPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Anharmonicity in grain boundary energy for Al: Thermodynamic integration with artificial-neural-network potential2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Atomic structures of grain boundaries for Si and Ge: A simulated annealing method with artificial-neural-network interatomic potentials2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: its ability to predict the atomic structures, energetics and lattice vibrational properties for Al2023

Author(s)

Journal Title

Data Source

[Journal Article] Electronic and atomic structures of Shockley-partial dislocations in CdX (X = S, Se and Te)2023

Author(s)

Journal Title

DOI

ISSN

Year and Date

Language

Data Source

[Journal Article] Grain boundary segregation of Y and Hf dopants in α-Al<sub>2</sub>O<sub>3</sub>: A Monte Carlo simulation with artificial-neural-network potential and density-functional-theory calculation2023

Author(s)

Journal Title

DOI

ISSN

Year and Date

Language

Data Source

[Journal Article] Atomic and electronic structure of grain boundaries in a-Al2O3: A combination of machine learning, first-principles calculation and electron microscopy2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: its ability to predict the atomic structures, energetics and lattice vibrational properties for Al2023

Author(s)

Journal Title

Data Source

[Journal Article] Grain-boundary thermodynamics with artificial-neural-network potential: Its ability to predict the atomic structures, energetics, and lattice vibrational properties for Al2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Atomic structures and stability of finite-size extended interstitial defects in silicon: Large-scale molecular simulations with a neural-network potential2022

Author(s)

YOKOI Tatsuya 横井達矢

Yokoi Tatsuya 横井達矢

[Presentation] 格子欠陥特性の高精度予測に向けた機械学習記述子および原子間ポテンシャルの構築2023

[Presentation] 格子欠陥特性の高精度予測に向けた機械学習記述子および原子間ポテンシャルの構築2023

[Presentation] 格子欠陥の原子構造・特性の予測に向けたニューラルネットワーク記述子および原子間ポテンシャルの構築2022

[Presentation] 格子欠陥の原子構造・特性の予測に向けたニューラルネットワーク記述子および原子間ポテンシャルの構築2022