  Low lattice thermal conductivity is essential for high thermoelectric
performance of a material. Lattice thermal conductivity is often computed using
density functional theory (DFT), typically at a high computational cost.
Training machine learning models to predict lattice thermal conductivity could
offer an effective procedure to identify low lattice thermal conductivity
compounds. However, in doing so, we must face the fact that such compounds can
be quite rare and distinct from those in a typical training set. This
distinctness can be problematic as standard machine learning methods are
inaccurate when predicting properties of compounds with features differing
significantly from those in the training set. By computing the lattice thermal
conductivity of 122 half-Heusler compounds, using the temperature-dependent
effective potential method, we generate a data set to explore this issue. We
first show how random forest regression can fail to identify low lattice
thermal conductivity compounds with random selection of training data. Next, we
show how active selection of training data using feature and principal
component analysis can be used to improve model performance and the ability to
identify low lattice thermal conductivity compounds. Lastly, we find that
active learning without the use of DFT-based features can be viable as a
quicker way of selecting samples.
