使用机器学习原子间势研究纳米晶硅中的声子介导热输运
Phonon-Mediated Thermal Transport in Nanocrystalline Silicon Using Machine-Learning Interatomic Potentials
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中文总结 AI 辅助
研究结构复杂材料中声子介导热输运难题，结合高斯近似势、多原子团簇展开模型等，开发机器学习框架模拟纳米晶硅热输运，计算相关参数，揭示晶界热边界电阻特性，相比传统势，该框架描述更准确自洽，利于低维材料热输运建模。
中文摘要 AI 辅助
理解结构复杂材料中的声子介导热输运仍是下一代电子和纳米机械设备面临的核心挑战，晶界和界面无序强烈限制热耗散。经典原子间势虽能大规模模拟，但可转移性有限。本文结合高斯近似势（GAP）、多原子团簇展开（MACE）模型与晶格动力学计算及非平衡分子动力学（NEMD），开发机器学习框架模拟体相和纳米晶硅热输运。通过统一的Phonopy/Phono3py工作流程计算声子色散、寿命和晶格热导率。NEMD模拟量化纳米晶硅晶界热边界电阻及其对界面粗糙度和原子描述的敏感性。与Stillinger-Weber和Tersoff势相比，机器学习原子间势对体相和界面声子输运描述更准确、自洽，能对低维材料纳米尺度热输运进行更具预测性的建模。
英文摘要
Understanding phonon-mediated heat transport in structurally complex materials remains a central challenge for next-generation electronic and nanomechanical devices, where grain boundaries and interfacial disorder strongly limit thermal dissipation. Although classical interatomic potentials enable large-scale simulations, their limited transferability can lead to inaccuracies in vibrational properties and interfacial phonon scattering. In this work, we develop a machine learning-based framework for modeling thermal transport in bulk and nanocrystalline silicon by combining Gaussian approximation potential and multi-atomic cluster expansion models with lattice-dynamical calculations and molecular dynamics. Harmonic and anharmonic force constants derived from machine-learning interatomic potentials (MLIPs) are used within a unified Phonopy/Phono3py workflow to compute phonon dispersions, lifetimes, and lattice thermal conductivity, providing an internally consistent description of vibrational properties. In nanocrystalline silicon, non-equilibrium molecular dynamics simulations directly quantify the thermal boundary resistance associated with grain boundaries and reveal its sensitivity to interfacial roughness and the underlying interatomic description. Compared with the Stillinger-Weber and Tersoff potentials, the MLIPs provide a quantitatively accurate and internally consistent description of bulk and interfacial phonon transport, enabling better predictive modeling of nanoscale thermal transport in low-dimensional materials.
