Scholarlink

<p>The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm<sup>-3</sup> with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi<sub>0.5</sub>Na<sub>0.5</sub>)TiO<sub>3</sub>-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown&#xa0;strength of 95 kV mm<sup>-1</sup>. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm<sup>-3</sup> under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月1日

查看全文
  • Xingcheng Wang,
  • Ji Zhang,
  • Xingshuai Ma,
  • Huajie Luo,
  • Laijun Liu,
  • Hui Liu,
  • Jun Chen

摘要

The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi0.5Na0.5)TiO3-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown strength of 95 kV mm-1. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm-3 under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors.