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

TL;DR

PYSED is a Python package that leverages machine learning-driven molecular dynamics and spectral energy density methods to extract detailed phonon dispersion and lifetime data, advancing thermal transport analysis in diverse materials.

Contribution

Introduces PYSED, a novel Python tool integrating machine learning potentials and spectral energy density techniques for phonon analysis from MD simulations.

Findings

Accurately reveals phonon mode effects under strain in carbon nanotubes.

Effectively distinguishes transport regimes in metal-organic frameworks.

Captures quantum dynamics in silicon using path-integral trajectories.

Abstract

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.