Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
Physical Review Materials(2023)
摘要
Thermal conductivity is a fundamental material property that plays an
essential role in technology, but its accurate evaluation presents a challenge
for theory. In this work, we demonstrate the application of E(3)-equivariant
neutral network interatomic potentials within Green-Kubo formalism to determine
the lattice thermal conductivity in amorphous and crystalline materials. We
apply this method to study the thermal conductivity of germanium telluride
(GeTe) as a prototypical phase change material. A single deep learning
interatomic potential is able to describe the phase transitions between the
amorphous, rhombohedral and cubic phases, with critical temperatures in good
agreement with experiments. Furthermore, this approach accurately captures the
pronounced anharmonicity that is present in GeTe, enabling precise calculations
of the thermal conductivity. In contrast, the Boltzmann transport equation
including only three-phonon processes tends to overestimate the thermal
conductivity by approximately a factor of 2 in the crystalline phases.
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关键词
thermal conductivity,phase change materials,equivariant graph,interatomic potential,green-kubo
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