  Recent advances in machine learning have led to foundation models for
atomistic materials chemistry, potentially enabling quantum-accurate
descriptions of interatomic forces at reduced computational cost. These models
are benchmarked by predicting materials' properties over large databases;
however, these computationally intensive tests have been limited to basic
quantities related to harmonic phonons, leaving uncertainty about the
reliability for complex, technologically and experimentally relevant anharmonic
heat-conduction properties. Here we present an automated framework that relies
on foundation models to compute microscopic vibrational properties, and employs
them within the Wigner formulation of heat transport to predict the macroscopic
thermal conductivity in solids with arbitrary composition and structure. We
apply this framework with the foundation models M3GNet, CHGNet, MACE-MP-0, and
SevenNET to 103 diverse compounds, comparing predictions against
first-principles references and introducing a benchmark metric based on
conductivity. This framework paves the way for physics-aware, accurate
predictions of vibrational and thermal properties, and for uncovering materials
that violate semiclassical Boltzmann transport and feature exceptional
heat-shielding or thermoelectric performance.
