White dwarf models require accurate equations of state and ionic transport coefficients in the warm dense-matter regime, where kinetic theory models and tabulated equations of state are often inaccurate. Here spectral-partitioned density functional theory and machine-learned interatomic potentials are combined to perform large-scale, first-principles quantum molecular dynamics simulations of deuterated water $\left({\mathrm{D}}_2\mathrm{O}\right)$ near the principal Hugoniot. This approach retains Kohn-Sham accuracy while achieving orders-of-magnitude speedup, yielding converged equation of state and transport properties over a broad pressure and temperature range. The results reveal the thermodynamic conditions under which ionic transport models for interdiffusivity and shear viscosity converge and identify those in closest agreement with density functional theory benchmarks at temperatures in the warm dense-matter regime. The present framework extends first-principles transport calculations to higher temperatures than previously achieved and provides an efficient, scalable, and general approach for studying transport properties in complex multicomponent mixtures.

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• High-energy-density plasmas
• Plasma transport
• Stars
• Density functional theory
• Machine learning