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Seamlessly joining length scales: From atomistic thermal graphs to anisotropic continuum conductivity

C. Ugwumadu*

D. A. Drabold

R. M. Tutchton

  • Department of Physics and Astronomy, Nanoscale & Quantum Phenomena Institute, Ohio University, Ohio 45701, USA

  • *Contact author: cugwumadu@lanl.gov
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Phys. Rev. Materials 10, 053804 – Published 21 May, 2026

DOI: https://doi.org/10.1103/pfdp-s7ql

Abstract

Thermal transport in complex solids is governed by local structure, defects, and anisotropy, yet most continuum models still rely on oversimplified and 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 toolkit, the “Simulator Collection for Atomic-to-Continuum Scales (SCACS)”, which uses a graph neural network to predict SPTC on large atomic structures, coarse-grains 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 work demonstrates a general route for transferring atomistic transport information into device-scale thermal simulations with physics-based approximations.

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Phys. Rev. Materials 10, 053804– Published 21 May, 2026
  • Received 3 February 2026
  • Accepted 20 April 2026
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