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

TL;DR

This paper develops an accurate neural network-based interatomic potential for Fe-Si-O systems, enabling reliable molecular dynamics simulations of Earth's outer core conditions with DFT-level precision.

Contribution

It introduces a transferable ANN-ML potential trained on first-principles data for Fe-Si-O, suitable for high-pressure, high-temperature MD simulations.

Findings

The ANN-ML potential accurately reproduces liquid structure and dynamics.

It demonstrates transferability across binary and ternary liquid phases.

The method offers a computationally efficient alternative to DFT for complex systems.

Abstract

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.