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Using artificial neural-network machine learning (ANN-ML) to generateinteratomic potentials has been demonstrated to be a promising approach toaddress the long-standing challenge of accuracy versus efficiency in moleculardynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, weshow that accurate and transferable ANN-ML potentials can be developed forreliable MD simulations of materials at high-pressure and high-temperatureconditions of the Earth's outer core. The ANN-ML potential for Fe-Si-O systemis trained by fitting to the energies and forces of related binaries andternary liquid structures at high pressures and temperatures obtained byfirst-principles calculations based on density functional theory (DFT). We showthat the generated ANN-ML potential describes well the structure and dynamicsof liquid phases of this complex system. The efficient ANN-ML potential withDFT accuracy provides a promising scheme for accurate atomistic simulations ofstructures and dynamics of complex Fe-Si-O system in the Earth's outer core.

A deep machine learning potential for atomistic simulation of Fe-Si-O systems under Earth's outer core conditions