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arXiv 2509.08573cond-mat.mtrl-sci
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面向复杂系统的第一性原理分子动力学热导率计算加速方法

Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems

Institute of Materials Chemistry, TU Wien(维也纳工业大学材料化学研究所)

Sandro Wieser, YuJie Cen, Georg K. H. Madsen, Jesús Carrete

AI总结:

针对复杂体系第一性原理分子动力学热导率计算成本高、难收敛的问题,本研究分析不同降噪方法的适用场景,结合机器学习原子间势构建了适配多类体系的加速鲁棒计算工作流。

摘要:

复杂材料热输运的原子模拟成本高昂且难以收敛,为此学界已开发出多种适用于平衡分子动力学(MD)模拟的降噪技术。本研究选取具有多样结构与复杂声子谱的准一维体系InAs纳米线作为基准,分析了上述策略的性能。我们证实,对于低热导率体系,倒谱分析可在无需随意丢弃数据集部分内容的前提下降低计算需求,同时输出准确结果;但该方法处理高热导率体系时存在缺陷,会显著低估热导率数值。针对该场景,我们探讨了基于独立模拟不确定性传播的替代方法,指出必须纳入协方差矩阵的贡献才能实现误差的定量评估。将上述策略与机器学习原子间势(MLIPs)结合,可构建出适用于多类体系的加速鲁棒工作流,我们采用高迁移性MACE势开展的算例验证了这一点。

英文摘要:

Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to equilibrium molecular-dynamics (MD) simulations. We analyze the performance of those strategies, taking InAs nanowires as our benchmark due to the diverse structures and complex phonon spectra of these quasi-1D systems. We demonstrate how, for low-thermal-conductivity systems, cepstral analysis can reduce computational demands while still delivering accurate results that do not require discarding arbitrary parts of the dataset. However, issues with this approach are revealed when treating high-thermal-conductivity systems, where the thermal conductivity is significantly underestimated. We discuss alternative methods to be used in that situation, relying on uncertainty propagation from independent simulations. We show that the contributions of the covariance matrix have to be included for a quantitative assessment of the error. The combination of these strategies with machine-learning interatomic potentials (MLIPs) provides an accelerated, robust workflow applicable to a diverse set of systems, as our examples using a highly transferable MACE potential illustrate.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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