Oral presentation

V. Nuclear Fuel Cycle and Nuclear Materials » 501-2 Nuclear Fuel and the Irradiation Behavior

[2C01-07] Data Science, Fuel Research with Machine Learning

Thu. Sep 7, 2023 9:30 AM - 11:25 AM Room C (IB Bildg.1F IB015)

Chair:Tatsumi Arima(Kyushu Univ.)

9:30 AM - 9:45 AM

[2C01] New Developments in Nuclear Fuel Research through Integration with Data Science

(1) General Introduction

*Ken Kurosaki^1,2, Yuji Ohishi³, Masato Kato⁴, Tsuyoshi Yaita⁴, Toru Higuchi⁵, Kenji Konashi⁶ (1. Kyoto Univ., 2. Univ. of Fukui, 3. Osaka Univ., 4. JAEA, 5. NFD, 6. Tohoku Univ.)

9:45 AM - 10:00 AM

[2C02] New Developments in Nuclear Fuel Research through Integration with Data Science

(2)Design of machine learning models for highly accurate prediction of thermal conductivity

*Koki Takeichi¹, Ken Kurosaki^1,4, Masaya Kumagai^1,3, Yuji Ohishi² (1. Kyoto Univ., 2. Osaka Univ., 3. SAKURA internet Inc., 4. Univ. of Fukui)

10:00 AM - 10:15 AM

[2C03] New Developments in Nuclear Fuel Research through Integration with Data Science

(3)Development of neural network potential of UO₂

*Kenji Konashi¹, Nobuhiko Kato², Kazuki Mori², Ken Kurosaki³ (1. Tohoku University, 2. CTC, 3. Kyoto University)

10:15 AM - 10:30 AM

[2C04] New Developments in Nuclear Fuel Research through Integration with Data Science

(4) Development of machine-learning gap conductance model

*Masato Kato¹, Tomoyuki Uwaba¹, Masashi Watanabe¹, Shun Hirooka¹, Kenji Konashi², Ken Kurosaki^3,4 (1. JAEA, 2. Tohoku Univ., 3. Kyoto Univ., 4. Univ. of Fukui )

10:30 AM - 10:45 AM

[2C05] New Developments in Nuclear Fuel Research through Integration with Data Science

(5)Fabrication and Characterization of Metallic Uranium Compounds (UFe₃B₂, USiNi) Prepared via Spark Plasma Sintering

*Yifan Sun¹, Yuji Miyawaki², Yuji Ohishi², Hiroaki Muta², Shun Fujieda², Ken Kurosaki^1,3 (1. Kyoto Univ., 2. Osaka Univ., 3. Univ. of Fukui)

10:45 AM - 11:00 AM

[2C06] New Developments in Nuclear Fuel Research through Integration with Data Science

(6) Development of synchrotron radiation XRD measurement system for gas-floating melts

*Tsuyoshi Yaita¹, Tohru Kobayashi¹, Hajime Tanida¹, Yuji Ohishi², Ken Kurosaki³, Kenji Konashi⁴ (1. JAEA, 2. Osaka Univ., 3. Kyoto Univ., 4. Tohoku Univ.)

11:00 AM - 11:15 AM

[2C07] New Developments in Nuclear Fuel Research through Integration with Data Science

(7) Synthesis of Uranium Related Oxide to Verify Thermal Conductivity Predicted by Materials Informatics

*Atsushi Ohuchi¹, Yoichi Endo¹, Yusuke Miura¹, Toru Higuchi¹, Ken Kurosaki² (1. NFD, 2. Kyoto Univ.)

Time reserved for Chair (11:15 AM - 11:25 AM)

Session information

[2C01-07] Data Science, Fuel Research with Machine Learning

[2C01] New Developments in Nuclear Fuel Research through Integration with Data Science

(1) General Introduction

[2C02] New Developments in Nuclear Fuel Research through Integration with Data Science

(2)Design of machine learning models for highly accurate prediction of thermal conductivity

[2C03] New Developments in Nuclear Fuel Research through Integration with Data Science

(3)Development of neural network potential of UO₂

[2C04] New Developments in Nuclear Fuel Research through Integration with Data Science

(4) Development of machine-learning gap conductance model

[2C05] New Developments in Nuclear Fuel Research through Integration with Data Science

(5)Fabrication and Characterization of Metallic Uranium Compounds (UFe₃B₂, USiNi) Prepared via Spark Plasma Sintering

[2C06] New Developments in Nuclear Fuel Research through Integration with Data Science

(6) Development of synchrotron radiation XRD measurement system for gas-floating melts

[2C07] New Developments in Nuclear Fuel Research through Integration with Data Science

(7) Synthesis of Uranium Related Oxide to Verify Thermal Conductivity Predicted by Materials Informatics

Authentication

Session information

[2C01-07] Data Science, Fuel Research with Machine Learning

[2C01] New Developments in Nuclear Fuel Research through Integration with Data Science

(1) General Introduction

[2C02] New Developments in Nuclear Fuel Research through Integration with Data Science

(2)Design of machine learning models for highly accurate prediction of thermal conductivity

[2C03] New Developments in Nuclear Fuel Research through Integration with Data Science

(3)Development of neural network potential of UO2

[2C04] New Developments in Nuclear Fuel Research through Integration with Data Science

(4) Development of machine-learning gap conductance model

[2C05] New Developments in Nuclear Fuel Research through Integration with Data Science

(5)Fabrication and Characterization of Metallic Uranium Compounds (UFe3B2, USiNi) Prepared via Spark Plasma Sintering

[2C06] New Developments in Nuclear Fuel Research through Integration with Data Science

(6) Development of synchrotron radiation XRD measurement system for gas-floating melts

[2C07] New Developments in Nuclear Fuel Research through Integration with Data Science

(7) Synthesis of Uranium Related Oxide to Verify Thermal Conductivity Predicted by Materials Informatics

Authentication

(3)Development of neural network potential of UO₂

(5)Fabrication and Characterization of Metallic Uranium Compounds (UFe₃B₂, USiNi) Prepared via Spark Plasma Sintering