Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
arxiv(2024)
摘要
Machine learned potentials (MLPs) have been widely employed in molecular
dynamics (MD) simulations to study thermal transport. However, literature
results indicate that MLPs generally underestimate the lattice thermal
conductivity (LTC) of typical solids. Here, we quantitatively analyze this
underestimation in the context of the neuroevolution potential (NEP), which is
a representative MLP that balances efficiency and accuracy. Taking crystalline
silicon, GaAs, graphene, and PbTe as examples, we reveal that the fitting
errors in the machine-learned forces against the reference ones are responsible
for the underestimated LTC as they constitute external perturbations to the
interatomic forces. Since the force errors of a NEP model and the random forces
in the Langevin thermostat both follow a Gaussian distribution, we propose an
approach to correcting the LTC by intentionally introducing different levels of
force noises via the Langevin thermostat and then extrapolating to the limit of
zero force error. Excellent agreement with experiments is obtained by using
this correction for all the prototypical materials over a wide range of
temperatures. Based on spectral analyses, we find that the LTC underestimation
mainly arises from increased phonon scatterings in the low-frequency region
caused by the random force errors.
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