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

Abstract

ABSTRACTAdvanced nuclear fuels are designed to offer improved performance and accident tol- erance, with an emphasis on achieving higher thermal conductivity. While promising fuel candidates like uranium nitrides, carbides, and silicides have been widely stud- ied, 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 consists 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 con- ductivity 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 ma- chine learning in the field of nuclear science, accelerating the discovery of advanced nuclear materials.KEYWORDS: Advanced nuclear fuelsMachine learningthermal conductivityDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. 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.