Machine learning prediction of thermal transport in porous media with physics-based descriptors

•The significant effect of pore distribution and shape on the effective thermal conductivities of porous media are identified.•Five structural descriptors with explicit physical meanings are proposed: shape factor, bottleneck, channel factor, perpendicular nonuniformity, and dominant paths.•These descriptors effectively quantify the anisotropy of pore morphology and strongly correlate with effective thermal conductivities.•The proposed descriptors are incorporated into machine learning models to predict the effective thermal conductivity of porous media and show significantly improved accuracy than using porosity alone.