  Using artificial neural-network machine learning (ANN-ML) to generate
interatomic potentials has been demonstrated to be a promising approach to
address the long-standing challenge of accuracy versus efficiency in molecular
dynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, we
show that accurate and transferable ANN-ML potentials can be developed for
reliable MD simulations of materials at high-pressure and high-temperature
conditions of the Earth's outer core. The ANN-ML potential for Fe-Si-O system
is trained by fitting to the energies and forces of related binaries and
ternary liquid structures at high pressures and temperatures obtained by
first-principles calculations based on density functional theory (DFT). We show
that the generated ANN-ML potential describes well the structure and dynamics
of liquid phases of this complex system. The efficient ANN-ML potential with
DFT accuracy provides a promising scheme for accurate atomistic simulations of
structures and dynamics of complex Fe-Si-O system in the Earth's outer core.
