Graph neural network prediction of temperature-dependent hydrogen diffusion and thermal conductivity tensors of tungsten containing helium bubbles and grain boundaries
Helium bubbles and grain boundaries in tungsten plasma-facing components alter hydrogen-isotope transport and thermal conduction by orders of magnitude, yet evaluating these transport properties for a given microstructure requires hours of molecular dynamics (MD) per configuration. We present a graph neural network surrogate that maps a tungsten atomic configuration containing helium bubbles and grain boundaries directly to the full $3\times3$ symmetric tensors of the hydrogen diffusion coefficient $D_H(T)$ and the thermal conductivity $\kappa(T)$ at arbitrary temperature. Anisotropy is captured by a rotation-equivariant tensor pooling layer; temperature enters through predicted temperature-independent parameters (an Arrhenius pair $(D_0,E_a)$, a phonon conductivity tensor, and a defect residual resistivity) expanded analytically via the Arrhenius and Wiedemann-Franz-Matthiessen relations. Training labels for 635 microstructures are generated with an embedded-atom-method potential (Green-Kubo conductivity and multi-temperature tracer diffusion); the electronic channel is calibrated against published irradiation-degradation measurements, and the pipeline is anchored to a first-principles machine-learning potential (VASP+FLARE) through paired MD calibration runs and an active-learning loop. The learned activation energies (median 0.21 eV, rising in bubble and grain-boundary structures) reproduce literature hydrogen migration barriers and trapping physics, and the equivariant pooling keeps predictions consistent across arbitrarily oriented sub-blocks. Coupled finite-element thermal-hydrogen analyses driven by the surrogate show that conductivity degradation changes predicted hydrogen permeation by a factor of 2.5 through the temperature field. The model returns both tensors in milliseconds, enabling microstructure-resolved transport input for component-scale analyses of fusion divertors.