Atomic-level study of dose-dependent lattice thermal conductivity degradation in irradiated tungsten

Abstract

Plasma-facing components (PFCs) in future fusion reactors must withstand extreme environments, making tungsten (W) a prime candidate material due to its high thermal conductivity, high melting temperature, low sputtering yield, low tritium absorption. However, neutron irradiation introduces complex nanostructural defects that severely degrade these critical properties. In this work, we use Non-Equilibrium Molecular Dynamics (NEMD) simulations coupled with the Creation Relaxation Algorithm (CRA) to investigate the lattice thermal conductivity (kappa) of W across a wide range of irradiation doses. Systems treated with different doses up to 1.8 canonical displacements per atom (cdpa) were simulated to generate and study diverse defect structures, including vacancies and self-interstitial atom (SIA) clusters. By correlating spatially-resolved temperature gradients with local defect concentrations, we show that even small changes in defect type and distribution significantly affect thermal transport. We employ Machine Learning (ML) models to develop a quantitative relationship between the defect features and the resulting thermal conductivity degradation. The ML analysis identifies vacancy density as the dominant structural factor controlling thermal transport. We found a significant, nonlinear suppression of kappa with increasing defect density. Predictive multi-defect mapping built upon our ML model resolved a step-like degradation transition at a critical vacancy threshold of 0.05 at.%, beyond which phonon scattering completely saturated and capped the maximum relative lattice thermal conductivity degradation at approximately 30 at.%. This integrated simulation and ML approach provides a powerful, predictive framework for quantifying the complex relationship between structure and thermal conductivity in irradiated fusion materials.

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