[2E01-04] データ科学・機械学習による燃料研究・高速炉リサイクル

[2E01] New Developments in Nuclear Fuel Research through Integration with Data Science

(8) Harnessing Machine Learning for the Discovery of High Thermal Conductivity Fuel Candidates

*Yifan Sun¹, Masaya Kumagai¹, Mingyu Jin¹, Eriko Sato¹, Masako Aoki¹, Yuji Ohishi², Ken Kurosaki^1,3 (1. Kyoto Univ., 2. Osaka Univ., 3. Univ. of Fukui)

キーワード：Advanced nuclear fuel, Machine learning, Thermal conductivity

In the wake of the Fukushima Daiichi Nuclear Power Plant incident, the development of advanced nuclear fuels has become a priority. While promising high-density fuels like UN and U₃Si₂ have been proposed, they are still far from commercially applicable, underscoring the need to explore a broader range of uranium compounds. Our study aims to expedite this exploration process by introducing a machine learning model capable of identifying uranium compounds with high thermal conductivity. This classification model was trained on 168,916 data points to predict thermal conductivity based on a compound’s composition and temperature. The model successfully identified 119 stable uranium compounds with thermal conductivities exceeding 15 W/mK, offering a significant leap forward in the search for advanced nuclear fuels.