Work

Physics-guided multi-task learning for predicting thermophysical properties of Ag-In-Cd absorber alloys with extremely small data

Year: 2025

Type: article

Abstract: Machine learning (ML) techniques have recently shown great promise in advancing nuclear materials research. However, the application of ML in this field is often limited by the scarcity of experimental data due to the high cost and complexity of irradiation experiments. In this study, we propose a physics-guided multi-task neural network (PGMTNN) framework to accurately predict the thermophysical properties of Ag-In-Cd control rod alloys under neutron irradiation, using extremely limited datasets. The PGMTNN model incorporates fundamental physical laws as soft constraints within a multi-task learning architecture, enabling the simultaneous prediction of density, specific heat capacity, thermal diffusivity, and thermal conductivity. This approach effectively captures the intrinsic correlations among different material properties and ensures physical consistency of the predictions. Compared with traditional ML methods and the transformer-based foundation model, the PGMTNN demonstrates superior performance, achieving an average coefficient of determination exceeding 0.92 on the testing set. These results underscore the potential of physics-guided, multi-task ML frameworks for accurate property prediction and reliable design of advanced absorber materials under small-data conditions.

Source: Nuclear Engineering and Technology

DOI: 10.1016/j.net.2025.104061

Authors: Yi-Rui Hu, Xiaoya Wang, Zhaohe Gao, Hongxing Xiao, Hongsheng Chen, Jie Xiong

Institutions: Shanghai University, Harbin Institute of Technology, Nuclear Power Institute of China

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $200

Funders: National Natural Science Foundation of China, National Science and Technology Major Project