Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
Yanzhou Wang, Zheyong Fan, Ping Qian, Miguel A. Caro, Tapio, Ala-Nissila

TL;DR
This study uses machine-learned molecular dynamics to analyze how density affects thermal conductivity in nanoporous and amorphous carbon, revealing different dependence patterns linked to structural motifs and order.
Contribution
It introduces a large-scale MD simulation approach with a machine-learned potential to systematically study structure-dependent thermal transport in disordered carbon.
Findings
Thermal conductivity shows linear density dependence in nanoporous carbon.
Amorphous carbon exhibits superlinear density dependence of thermal conductivity.
Structural motifs and order significantly influence heat transport properties.
Abstract
Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic mechanisms is lacking. Here we study the structure-dependent thermal conductivity of disordered carbon by employing molecular dynamics (MD) simulations driven by a machine-learned interatomic potential based on the efficient neuroevolution potential approach. Using large-scale MD simulations, we generate realistic nanoporous carbon (NP-C) samples with density varying from to g cm dominated by sp motifs, and amorphous carbon (a-C) samples with density varying from to g cm exhibiting mixed sp and sp motifs. Structural properties including short- and medium-range order are characterized by atomic…
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Taxonomy
TopicsMachine Learning in Materials Science · Thermal properties of materials · Nanopore and Nanochannel Transport Studies
