Intrinsic structure of relaxor ferroelectrics from first principles

TL;DR

This paper introduces FIRE-Swap, a first-principles framework utilizing machine-learning interatomic potentials to analyze the intrinsic compositional structures of relaxor ferroelectrics, revealing key mesoscale features linked to their unique properties.

Contribution

The study develops FIRE-Swap, a novel first-principles method that accurately predicts chemical ordering and mesoscale structures in complex perovskites, advancing understanding of relaxor ferroelectricity.

Findings

FIRE-Swap predicts rock-salt-like order in PMN, absent in PZT and PST.

Identifies Nb-cluster morphology in PMN.

Reveals interconnected polar nanoregions within Nb clusters.

Abstract

We develop FIRE-Swap, a first-principles framework for sampling intrinsic compositional structures in complex perovskites with machine-learning interatomic potentials (MLIPs). Using both dedicated and universal MLIPs, we study the relaxor lead magnesium niobate (PMN) and the solid solutions lead zirconate titanate (PZT) and lead strontium titanate (PST). Across MLIP models and exchange-correlation approximations, FIRE-Swap robustly predicts a rock-salt-like chemical order in PMN, which is absent in PZT and PST with the same mixing ratio, consistent with experiments. We further identify in PMN a distinct Nb-cluster morphology. Interconnected, non-coarsened polar nanoregions are found within Nb clusters, providing a mesoscale basis for understanding relaxor ferroelectricity.