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Machine learning techniques are used to explore the intrinsic origins of thehydrodynamic thermal transport and to find new materials interesting forscience and engineering. The hydrodynamic thermal transport is governedintrinsically by the hydrodynamic scale and the thermal conductivity. Thecorrelations between these intrinsic properties and harmonic and anharmonicproperties, and a large number of compositional (290) and structural (1224)descriptors of 131 crystal compound materials are obtained, revealing some ofthe key descriptors that determines the magnitude of the intrinsic hydrodynamiceffects, most of them related with the phonon relaxation times. Then, a trainedblack-box model is applied to screen more than 5000 materials. The resultsidentify materials with potential technological applications. Understanding theproperties correlated to hydrodynamic thermal transport can help to find newthermoelectric materials and on the design of new materials to ease the heatdissipation in electronic devices.

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach