Understanding the coupling between structural phase transitions and thermal transport is essential for designing functional materials with tunable properties. Here, we investigate this interplay in ${\mathrm{CaSnF}}_6$ by combining first-principles calculations with a machine-learned neuroevolution potential that enables large-scale molecular dynamics simulations across a wide temperature range. The simulations accurately capture the first-order structural phase transition and associated lattice dynamics. We show that the negative thermal expansion originates from low-energy rigid unit modes involving cooperative rotations of corner-sharing ${[\mathrm{CaF}}_6{]}^{4-}$ and ${[\mathrm{SnF}}_6{]}^{2-}$ octahedras, which induce bond-angle bending and volume contraction. At the same time, strong anharmonicity, dominated by four-phonon scattering, plays a central role in suppressing lattice thermal conductivity $\left({\kappa }_L\right)$. Crucially, nonequilibrium simulations reveal a pronounced nonmonotonic anomaly in ${\kappa }_L$ near the phase transition, deviating from the conventional $\sim 1/T^{\alpha }$ behavior and providing direct transport evidence of lattice reconstruction. These results establish a unified mechanism linking lattice geometry, anharmonic vibrational dynamics, and thermal transport, and highlight the potential of machine-learned potentials for bridging atomic-scale phase transitions with macroscopic transport properties.

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• Phase transitions
• Thermal conductivity
• Thermal expansion
• Thermal properties
• Density functional calculations
• Machine learning
• Molecular dynamics