Designing Pb-Free High-Entropy Relaxor Ferroelectrics with Machine Learning Assistance for High Energy Storage.

Journal: Journal of the American Chemical Society
Published Date: Jul 23, 2025

Abstract

High-entropy tactics present exceptional promise in advancing the dielectric energy storage of relaxor ferroelectrics, thereby benefiting various pulsed-power electronic systems. However, their vast composition space poses challenges in the rational design of a high-performance system. Herein, we present a machine learning-supplemented strategy to design high-entropy relaxors, demonstrating an ultrahigh energy-storage density of 17.2 J cm and high efficiency of 87% at a high breakdown strength of 79 kV mm. By integrating six -site and one -site critical intrinsic features of constituent ions, deduced from a constructed random forest regression model, the (BiNaKBa)(Ti,Hf)O high-entropy system is identified. Atomic-level local structural analysis reveals that incorporating these certified cations, with diverse local polar and lattice construction characteristics, results in a highly fluctuating local polarization structure. This favorable structure is characterized by pronounced orientation disorder and a broadly distributed length of unit-cell polarization vectors within the expanded lattice framework. Macroscopically, the optimized relaxor displays high dielectric susceptibility and large resistance. Moreover, a large discharge energy density of 5.8 J cm and power energy density of 447 MW cm, along with outstanding operational stability, are achieved. This study presents a data-driven model to explore complex intrinsic features and facilitate the design of high-performance relaxors.

Authors

  • Banghua Zhu
    Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
  • Xingcheng Wang
    Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
  • Ji Zhang
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Huajie Luo
    Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
  • Laijun Liu
    College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China.
  • Joerg C Neuefeind
    Chemical and Engineering Materials Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.

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