  Calculations of heat transport in crystalline materials have recently become
mainstream, thanks to machine-learned interatomic potentials that allow for
significant computational cost reductions while maintaining the accuracy of
first-principles calculations. Moment tensor potentials (MTP) are among the
most efficient and accurate models in this regard. In this study, we
demonstrate the application of MTP to the calculation of the lattice thermal
conductivity of alpha and beta Ga2O3. Although MTP is commonly employed for
lattice thermal conductivity calculations, the advantages of applying the
active learning methodology for potential generation is often overlooked. Here,
we emphasize its importance and illustrate how it enables the generation of a
robust and accurate interatomic potential while maintaining a moderate-sized
training dataset.
