  Ultrahigh lattice thermal conductivity materials hold great importance since
they play a critical role in the thermal management of electronic and optical
devices. Models using machine learning can search for materials with
outstanding higher-order properties like thermal conductivity. However, the
lack of sufficient data to train a model is a serious hurdle. Herein we show
that big data can complement small data for accurate predictions when
lower-order feature properties available in big data are selected properly and
applied to transfer learning. The connection between the crystal information
and thermal conductivity is directly built with a neural network by
transferring descriptors acquired through a pre-trained model for the feature
property. Successful transfer learning shows the ability of extrapolative
prediction and reveals descriptors for lattice anharmonicity. Transfer learning
is employed to screen over 60000 compounds to identify novel crystals that can
serve as alternatives to diamond.
