  The application of first-principles calculations for predicting lattice
thermal conductivity (LTC) in crystalline materials, in conjunction with the
linearized phonon Boltzmann equation, has gained increasing popularity. In this
calculation, the determination of force constants through first-principles
calculations is critical for accurate LTC predictions. For material
exploration, performing first-principles LTC calculations in a high-throughput
manner is now expected, although it requires significant computational
resources. To reduce computational demands, we integrated polynomial machine
learning potentials on-the-fly during the first-principles LTC calculations.
This paper presents a systematic approach to first-principles LTC calculations.
We designed and optimized an efficient workflow that integrates multiple
modular software packages. We applied this approach to calculate LTCs for 103
compounds of the wurtzite, zincblende, and rocksalt types to evaluate the
performance of the polynomial machine learning potentials in LTC calculations.
We demonstrate a significant reduction in the computational resources required
for the LTC predictions.
