On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity
arxiv(2024)
摘要
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 while preserving accuracy
moderately, 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,
while maintaining reasonable accuracy.
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