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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	E-25: Feature Engineering for Construction of High-accuracy Thermal Conductivity Prediction Model for Uranium Compounds
Author(s)	koki Takeichi, Masaya Kumagai, Yuji Ohishi, Ken Kurosaki
On-Site Speaker (Planned)	koki Takeichi
Abstract Scope	To explore advanced nuclear fuels with high thermal conductivity, we previously constructed a machine learning model that can directly predict thermal conductivity based on chemical composition. However, the prediction accuracy was limited by using only input features based on chemical composition. Therefore, in this study, we aimed to improve the accuracy of thermal conductivity prediction by adding structural information as input features. For the prediction of thermal conductivity, we used Starrydata2 database (https://www.starrydata2.org/), which records experimental properties, but the database does not record crystal structure information. Therefore, using Materials Project database (https://next-gen.materialsproject.org/), a machine learning model was preliminarily constructed to predict the crystal structure information from the chemical composition. The crystal structure information predicted by the pre-learned model was added to Starrydata2 to create an original dataset, and thermal conductivity was predicted. As a result, it was found that lattice constant is the feature that improves prediction accuracy the most.
Proceedings Inclusion?	Planned:
Keywords	Nuclear Materials, Machine Learning, Characterization

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