PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations

T Liang and WW Jiang and K Xu and HK Bu and ZY Fan and WE Ouyang and JB Xu, JOURNAL OF APPLIED PHYSICS, 138, 075101 (2025).

DOI: 10.1063/5.0278798

Machine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials. However, extracting phonon-mode- resolved insights from these simulations remains a critical challenge. Here, we introduce pysed, a Python-based package built on the spectral energy density (SED) method, designed to efficiently compute kinetic- energy-weighted phonon dispersion and extract phonon lifetime from large-scale MD simulation trajectories. By integrating high-accuracy machine-learned neuroevolution potential (NEP) models, we validate and showcase the effectiveness of the implemented SED method across systems of varying dimensionalities. Specifically, the NEP-driven MD-SED accurately reveals how phonon modes are affected by strain in carbon nanotubes, as well as by interlayer coupling strengths and the twist angles in two-dimensional molybdenum disulfide. For three-dimensional systems, the SED method effectively establishes the thermal transport regime diagram for metal-organic frameworks, distinguishing between particlelike and wavelike propagation regions. Moreover, using bulk silicon as an example, we show that phonon SED can efficiently capture quantum dynamics based on path-integral trajectories. The pysed package bridges MD simulations with detailed phonon-mode insights, delivering a robust tool for investigating thermal transport properties with detailed mechanisms across various materials. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0International (CC BY-NC) license (https://creativecommons.org/licenses/by- nc/4.0/).https://doi.org/10.1063/5.0278798

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