标题： PYSED：从分子动力学模拟中提取动能加权声子色散和寿命的工具 显示英文标题

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

摘要： 机器学习势驱动的分子动力学（MD）模拟显著提高了多种材料热输运性质的预测准确性。 然而，从这些模拟中提取声子模分辨的见解仍然是一个关键挑战。 在此，我们介绍了PYSED，一个基于光谱能量密度（SED）方法的Python包，旨在高效计算动能加权的声子色散并从大规模MD模拟轨迹中提取声子寿命。 通过集成高精度的机器学习神经进化势（NEP）模型，我们在不同维度的系统中验证并展示了所实现的SED方法的有效性。 具体而言，NEP驱动的MD-SED准确揭示了应变如何影响碳纳米管中的声子模式，以及二维二硫化钼中层间耦合强度和扭转角度对声子模式的影响。 对于三维系统，SED方法有效地建立了金属有机框架的热输运区域图，区分了粒子状和波状传播区域。 此外，以体硅为例，我们展示了声子SED可以高效地捕捉基于路径积分轨迹的量子动力学。 PYSED包将MD模拟与详细的声子模见解联系起来，为研究各种材料中的热输运性质及其详细机制提供了一个强大的工具。 显示英文摘要

摘要： 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.