  Lattice thermal conductivity (TC) of semiconductors is crucial for various
applications, ranging from microelectronics to thermoelectrics. Data-driven
approach can potentially establish the critical composition-property
relationship needed for fast screening of candidates with desirable TC, but the
small number of available data remains the main challenge. TC can be
efficiently calculated using empirical models, but they have inferior accuracy
compared to the more resource-demanding first-principles calculations. Here, we
demonstrate the use of transfer learning (TL) to improve the machine learning
models trained on small but high-fidelity TC data from experiments and
first-principles calculations, by leveraging a large but low-fidelity data
generated from empirical TC models, where the trainings on high- and
low-fidelity TC data are treated as different but related tasks. TL improves
the model accuracy by as much as 23% in R2 and reduces the average factor
difference by as much as 30%. Using the TL model, a large semiconductor
database is screened, and several candidates with room temperature TC > 350
W/mK are identified and further verified using first-principles simulations.
This study demonstrates that TL can leverage big low-fidelity data as a proxy
task to improve models for the target task with high-fidelity but small data.
Such a capability of TL may have important implications to materials
informatics in general.
