  Thermal conductivity is a fundamental material property but challenging to
predict, with less than 5% out of about $10^5$ synthesized inorganic materials
being documented. In this work, we extract the structural chemistry that
governs lattice thermal conductivity, by combining graph neural networks and
random forest approaches. We show that both mean and variation of unit-cell
configurational properties, such as atomic volume and bond length, are the most
important features, followed by mass and elemental electronegativity. We chart
the structural chemistry of lattice thermal conductivity into extended
van-Arkel triangles, and predict the thermal conductivity of all known
inorganic materials in the Inorganic Crystal Structure Database. For the
latter, we develop a transfer learning framework extendable for other
applications.
