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<p>The accurate prediction of irradiation embrittlement in reactor pressure vessel (RPV) steel remains challenging. Here, we develop a multi-head attention network (MHANet) and target the service-relevant shift in ductile-brittle transition temperature (ΔDBTT) co-varying with temperature, flux, and fluence. MHANet treats each input column as a token, learns feature dependencies via attention, and yields accurate predictions with transparent attribution. Trained/validated on 1266 IVAR+NUREG samples, MHANet achieves MAE of 5.63 <sup>∘</sup>C(IVAR) and 16.21 <sup>∘</sup>C (NUREG), and outperforms a baseline multilayer perceptron model. The learned dependencies are found to be physically consistent with the coupled effects by which ΔDBTT is governed, including point defects, solute-defect interactions (particularly involving Cu, Ni, and P), and irradiation-induced precipitation. (i) a higher temperature mitigates embrittlement; (ii) at a fixed fluence, the reduction in ΔDBTT with increasing flux is attributed to the competition between steady-state defect concentrations and the available evolution time; (iii) threshold-like synergies emerge-ΔDBTT increases sharply when Ni ≈ 0.9–1.1 wt.% and Cu (≥0.10 wt.%) or P is elevated. These results provide a coherent path from data-driven prediction to mechanism-aware interpretation and actionable criteria for RPV life assessment and alloy design.</p><p></p>

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Multi-head attention network for prediction of irradiation embrittlement in reactor pressure vessel steels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Chong Tian,
  • Jie Gao,
  • Dongyue Chen,
  • Zhengcao Li,
  • Shasha Lv

摘要

The accurate prediction of irradiation embrittlement in reactor pressure vessel (RPV) steel remains challenging. Here, we develop a multi-head attention network (MHANet) and target the service-relevant shift in ductile-brittle transition temperature (ΔDBTT) co-varying with temperature, flux, and fluence. MHANet treats each input column as a token, learns feature dependencies via attention, and yields accurate predictions with transparent attribution. Trained/validated on 1266 IVAR+NUREG samples, MHANet achieves MAE of 5.63 ∘C(IVAR) and 16.21 ∘C (NUREG), and outperforms a baseline multilayer perceptron model. The learned dependencies are found to be physically consistent with the coupled effects by which ΔDBTT is governed, including point defects, solute-defect interactions (particularly involving Cu, Ni, and P), and irradiation-induced precipitation. (i) a higher temperature mitigates embrittlement; (ii) at a fixed fluence, the reduction in ΔDBTT with increasing flux is attributed to the competition between steady-state defect concentrations and the available evolution time; (iii) threshold-like synergies emerge-ΔDBTT increases sharply when Ni ≈ 0.9–1.1 wt.% and Cu (≥0.10 wt.%) or P is elevated. These results provide a coherent path from data-driven prediction to mechanism-aware interpretation and actionable criteria for RPV life assessment and alloy design.