Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
Ting Liang, Penghua Ying, Ke Xu, Zhenqiang Ye, Chao Ling, Zheyong Fan,, and Jianbin Xu

TL;DR
This study develops a machine-learned potential for amorphous silica, enabling accurate molecular dynamics simulations that reveal how phonons and liquid-like diffusion contribute to temperature-dependent thermal transport.
Contribution
The paper introduces a highly efficient machine-learning potential that closely replicates experimental amorphous silica, allowing detailed analysis of thermal transport mechanisms across temperatures.
Findings
Phonons remain well-defined below the Ioffe-Regel crossover frequency.
Thermal conductivity increases at low temperatures due to phonon activity.
High-temperature thermal conductivity is governed by phonon scattering and liquid-like diffusion.
Abstract
Amorphous silica (a-SiO) is a foundational disordered material for which the thermal transport properties are important for various applications. To accurately model the interatomic interactions in classical molecular dynamics (MD) simulations of thermal transport in a-SiO, we herein develop an accurate yet highly efficient machine-learned potential model that allowed us to generate a-SiO samples closely resembling experimentally produced ones. Using the homogeneous nonequilibrium MD method and a proper quantum-statistical correction to the classical MD results, quantitative agreement with experiments is achieved for the thermal conductivities of bulk and 190 nm-thick a-SiO films over a wide range of temperatures. To interrogate the thermal vibrations at different temperatures, we calculated the current correlation functions corresponding to the transverse acoustic (TA)…
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Taxonomy
TopicsMachine Learning in Materials Science · Glass properties and applications · High-pressure geophysics and materials
