A multiclass classification model for predicting the thermal conductivity of uranium compounds

热导率 核燃料 欠采样 人工智能 过采样 核数据 计算机科学 材料科学 多类分类 机器学习 支持向量机 核工程 物理 核物理学 工程类 冶金 复合材料 中子 带宽(计算) 计算机网络
作者
Yifan Sun,Masaya Kumagai,Mingzhou Jin,Eimei Sato,Masayo Aoki,Yuji Ohishi,Ken Kurosaki
出处
期刊:Journal of Nuclear Science and Technology [Taylor & Francis]
卷期号:61 (6): 778-788 被引量:1
标识
DOI:10.1080/00223131.2023.2269974
摘要

ABSTRACTAdvanced nuclear fuels are designed to offer improved performance and accident tolerance, with an emphasis on achieving higher thermal conductivity. While promising fuel candidates like uranium nitrides, carbides, and silicides have been widely studied, the majority of uranium compounds remain unexplored. To search for potential candidates among these unexplored uranium compounds, we incorporated machine learning to accelerate the material discovery process. In this study, we trained a multiclass classification model to predict a compound's thermal conductivity based on 133 input features derived from element properties and temperature. The initial training data consist of over 160,000 processed thermal conductivity records from the Starrydata2 database, but a skewed data class distribution led the trained model to underestimate compound's thermal conductivity. Consequently, we addressed the issue of class imbalance by applying Synthetic Minority Oversampling TEchnique and Random UnderSampling, improving the recall for materials with thermal conductivity higher than 15 W/mK from 0.64 to 0.71. Finally, our best model is used to identify 119 potential advanced fuel candidates with high thermal conductivity among 774 stable uranium compounds. Our results underscore the potential of machine learning in the field of nuclear science, accelerating the discovery of advanced nuclear materials.KEYWORDS: Advanced nuclear fuelsmachine learningthermal conductivity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at https://github.com/AzarashiYifan/classification-uranium-thermal-conductivity.Additional informationFundingThis work was supported by MEXT Innovative Nuclear Research and Development Program Grant Number JPMXD0220354330 and JPMXD0222682541.
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