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Intrinsic structure of relaxor ferroelectrics from first principles

Xinyu Xu1,2,*, Kehan Cai3,*, Yubai Shi4, Peichen Zhong4, and Pinchen Xie1,†

  • *These authors contributed equally to this work.
  • †Contact author: pinchenxie@lbl.gov
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Phys. Rev. B 114, 024204 – Published 6 July, 2026Erratum Phys. Rev. B 114, 139901 (2026)

DOI: https://doi.org/10.1103/fz5h-1t5b

Abstract

We introduce FIRE-Swap, a first-principles-based framework for sampling representative compositional structures in complex perovskites using machine-learning interatomic potentials. By combining Monte Carlo chemical swaps with geometric relaxation, the method treats chemical and geometric degrees of freedom concurrently. Applied to lead magnesium niobate (PMN), lead zirconate titanate (PZT), and lead strontium titanate (PST), FIRE-Swap robustly predicts rock-salt-like chemical order in PMN, but not in the isovalent solid solutions PZT and PST with the same mixing ratio. For PMN, it further reveals anchored Nb clusters that host interconnected, noncoarsened polar nanoregions. These results provide a mesoscale structural basis for understanding relaxor ferroelectricity and establish FIRE-Swap as a practical route toward systematic, first-principles modeling of complex perovskites and related substitutionally disordered materials.

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Erratum

Erratum: Intrinsic structure of relaxor ferroelectrics from first principles [Phys. Rev. B 114, 024204 (2026)]

Xinyu Xu, Kehan Cai, Yubai Shi, Peichen Zhong, and Pinchen Xie
Phys. Rev. B 114, 139901 (2026)

Article Text

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Phys. Rev. B 114, 024204– Published 6 July, 2026
  • Received 23 November 2025
  • Revised 27 March 2026
  • Accepted 11 June 2026
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