设计无Pb高的Relaxor铁电与机器学习辅助高能量存储
Banghua Zhu1, Xingcheng Wang1, Ji Zhang2
1Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
Journal of the American Chemical Society
|July 23, 2025
概括
机器学习加速了高放松器的设计,用于先进的介电储能. 这种策略在脉冲动力电子中产生了超高的能量密度和效率.
科学领域:
- 材料科学
- 固态物理
- 介电材料
背景情况:
- 高材料提供了放松或铁电中增强介电能储存的潜力.
- 这些材料的庞大构成空间使得合理的设计更加复杂.
- 对于脉冲动力电子系统来说,
研究的目的:
- 为设计高放松电或铁电技术制定一个辅助机器学习的策略.
- 在这些材料中实现超高的储能密度和效率.
- 确定影响材料性能的主要内在特征.
主要方法:
- 使用机器学习方法,特别是随机森林回归模型,以识别构成性离子的关键内在特征.
- 集成六个A位点和一个B位点的离子特征用于材料设计.
- 进行原子级局部结构分析以了解极化行为.
主要成果:
- 确定了具有特殊介电储能特性的 (Bi2/5Na1/5K1/5Ba1/5) Ti,Hf) O3高系统.
- 实现了超高的储能密度17.2 J cm-3,87%的效率和79 kV mm-1的断裂强度.
- 观察到一个高度波动的局部极化结构,具有明显的方向障碍和分布式极化向量.
结论:
- 这种以数据驱动,机器学习为辅助的策略有效地在高放松器的复杂组合空间中进行导航.
- 优化的放松器具有出色的介电性质,高放电能量密度 (5.8 J cm−3) 和功率能量密度 (447 MW cm−3).
- 这种方法可以为先进的储能应用提供高性能介电材料的合理设计.
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