无缝连接长度尺度：从原子热图到各向异性连续体导热
Seamlessly joining length scales: From atomistic thermal graphs to anisotropic continuum conductivity
AI总结：
本文提出SCACS工具包，通过原子级热导率分解构建有限元模型，实现从原子到连续尺度的无缝连接，捕捉各向异性导热特性并保持热力学一致性。
摘要：
复杂固体中的热传输由局部结构、缺陷和各向异性决定，但大多数连续模型仍依赖于过于简化的均质导热系数。在这里，我们通过直接从位投影热导率（SPTC）构建有限元（FE）模型，将原子和连续描述连接起来，SPTC是格林-库波热导率的原子级分解。我们引入了一个新的工具包，即原子到连续尺度的模拟集合（SCACS），它使用图神经网络预测大原子结构上的SPTC，将这些场粗粒化为各向异性导热张量，并通过定制的、各向异性感知的自适应网格细化方案将其嵌入热流有限元方程中。应用于硅纳米结构时，所得到的有限元模型充当代表性体积元素，再现了本体导热性，并捕捉到界面和缺陷驱动的各向异性，同时保持热力学一致性。此外，SCACS预测了实验导电趋势和场。这一创新展示了一种新的、通用的途径，将原子传输信息转移到设备尺度的热模拟中，使用基于物理的近似方法。
英文摘要：
Thermal transport in complex solids is governed by local structure, defects, and anisotropy, yet most continuum models still rely on oversimplified, homogenized conductivities. Here, we bridge atomistic and continuum descriptions by building finite element (FE) models directly from the site-projected thermal conductivity (SPTC), an atomic-level decomposition of the Green-Kubo thermal conductivity. We introduce a new toolkit, the Simulator Collection for Atomic-to-Continuum Scales (SCACS), which uses a graph neural network to predict SPTC on large atomic structures, coarse-grain these fields into anisotropic conductivity tensors, and embeds them into the heat-flow FE equation with a customized, anisotropy-aware adaptive mesh refinement scheme. Applied to silicon nanostructures, the resulting FE models act as representative volume elements, reproduce bulk conductivities, and capture interfacial and defect-driven anisotropy while maintaining thermodynamic consistency. Additionally, SCACS predicts experimental conductance trends and fields. This innovation demonstrates a novel and general route for transferring atomistic transport information into device-scale thermal simulations with physics-based approximations.
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