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

Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the longstanding challenge of accuracy vs 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 the Fe-Si-O system is trained by fitting 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. In addition to binary systems (${\mathrm{Fe}}₁₈₉{\mathrm{Si}}₆₁, {\mathrm{Fe}}₁₈₉{\mathrm{O}}₆₁$, and ${\mathrm{Si}}₈₀{\mathrm{O}}₁₆₀$) and ternary systems (${\mathrm{Fe}}₁₈₉{\mathrm{Si}}₃₈{\mathrm{O}}₂₃$), whose snapshots are included in the training dataset, the reliability of the ANN-ML potential is validated in two other ternary systems (${\mathrm{Fe}}₁₈₉{\mathrm{Si}}₂₃{\mathrm{O}}₃₈$ and ${\mathrm{Fe}}₁₅₈{\mathrm{Si}}₁₄{\mathrm{O}}₂₈$), whose snapshots are not included in the training dataset. The efficient ANN-ML potential with DFT accuracy provides a promising scheme for accurate atomistic simulations of structures and dynamics of the complex Fe-Si-O system in the Earth's outer core.