近日，北京科技大学陈骏团队研究了基于机器学习的高能量存储无铅高熵弛豫铁电体设计。相关论文发表在2025年7月23日出版的《美国化学会志》上。

高熵策略在推进弛豫铁电体的介电能量存储方面表现出非凡的前景，从而使各种脉冲电力电子系统受益。然而，它们巨大的组成空间给高性能系统的合理设计带来了挑战。

研究组提出了一种以机器学习为补充的策略来设计高熵弛豫器，在79 kV mm^-1的高击穿强度下，展示了17.2 J cm^-3的超高储能密度和87%的高效率。通过构建随机森林回归模型，对组成离子的6个A位和1个B位关键特征进行积分，确定了（Bi_2/5Na_1/5K_1/5Ba_1/5）（Ti,Hf）O₃高熵系统。原子水平的局部结构分析表明，这些具有不同局域极性和晶格结构特征的阳离子的加入，导致了高度波动的局域极化结构。

这种有利结构的特点是明显的取向紊乱和在扩展的晶格框架内广泛分布的单位胞极化向量长度。从宏观上看，优化后的弛豫器具有较高的介电敏感性和较大的电阻。放电能量密度可达5.8 J cm^-3，功率能量密度可达447 MW cm^-3，具有良好的运行稳定性。该研究提出了一种数据驱动的模型来探索复杂的内在特征，并促进高性能弛豫器的设计。

附：英文原文

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

Author: Banghua Zhu, Xingcheng Wang, Ji Zhang, Huajie Luo, Laijun Liu, Joerg C. Neuefeind, Hui Liu, Jun Chen

Issue&Volume: July 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–3 and high efficiency of 87% at a high breakdown strength of 79 kV mm–1. By integrating six A-site and one B-site critical intrinsic features of constituent ions, deduced from a constructed random forest regression model, the (Bi2/5Na1/5K1/5Ba1/5)(Ti,Hf)O3 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–3 and power energy density of 447 MW cm–3, 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.

DOI: 10.1021/jacs.5c07213

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.5c07213