  Amorphous silicon (a-Si) is an important thermal-management material and also
serves as an ideal playground for studying heat transport in strongly
disordered materials. Theoretical prediction of the thermal conductivity of
a-Si in a wide range of temperatures and sample sizes is still a challenge.
Herein we present a systematic investigation of the thermal transport
properties of a-Si by employing large-scale molecular dynamics (MD) simulations
with an accurate and efficient machine-learned neuroevolution potential (NEP)
trained against abundant reference data calculated at the quantum-mechanical
density-functional-theory level. The high efficiency of NEP allows us to study
the effects of finite size and quenching rate in the formation of a-Si in great
detail. We find that it requires a simulation cell up to $64,000$ atoms (a
cubic cell with a linear size of 11 nm) and a quenching rate down to $10^{11}$
K s$^{-1}$ for fully convergent thermal conductivity. Structural properties,
including short- and medium-range order as characterized by the pair
correlation function, angular distribution function, coordination number, ring
statistics and structure factor are studied to demonstrate the accuracy of NEP
and to further evaluate the role of quenching rate. Using both the
heterogeneous and the homogeneous nonequilibrium MD methods and the related
spectral decomposition techniques, we calculate the temperature- and
thickness-dependent thermal conductivity values of a-Si and show that they
agree well with available experimental results from 10 K to room temperature.
Our results also highlight the importance of quantum effects in the calculated
thermal conductivity and support the quantum correction method based on the
spectral thermal conductivity.
