Assessing foundational atomistic models for iron alloys under Earth's core conditions

TL;DR

This study evaluates the performance of recent atomistic models in simulating iron alloys under Earth's core conditions, benchmarking their accuracy against ab initio calculations and identifying key limitations.

Contribution

It provides a comprehensive assessment of foundational atomistic models' capabilities and shortcomings for simulating Earth's core materials at extreme conditions.

Findings

MACE overestimates bcc iron stability

Models reproduce some structural properties but not all benchmarks

Lack of electronic excitation treatment limits model accuracy

Abstract

We assess the capability of recently developed foundational atomistic models (FAMs) to simulate iron alloys under the extreme pressures and temperatures of Earth's core. Static equations of state of hexagonal close-packed (hcp) and body-centered cubic (bcc) iron computed by 17 FAMs are benchmarked against ab initio calculations. Two representative models, MatterSim and MACE, are further evaluated for their ability to reproduce phonon spectra, liquid structure, and melting relations of iron at core conditions. While both models capture several key properties, MACE substantially overestimates the stability of bcc iron and fails to correctly describe the stability of hcp iron. Their performance is also examined for binary liquids, superionic phases, and a seven-component Fe-Ni-Si-S-O-H-C liquid. Although these FAMs were not explicitly trained on data from core conditions, they can reproduce several structural and dynamical properties across a wide range of compositions. However, none of the tested models consistently reproduces all first-principles benchmarks. By analyzing the origins of these discrepancies, we identify several limitations of current FAMs, particularly the lack of an explicit treatment of thermal electronic excitations, which significantly affect phase stability and thermodynamic properties under core conditions. We further discuss directions for improving FAMs to enable predictive simulations of core-forming materials under extreme conditions.