Wang Xingcheng, Zhang Ji, Ma Xingshuai, Luo Huajie, Liu Laijun, Liu Hui, Chen Jun
Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology Beijing, Beijing, 100083, China.
School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China.
Nat Commun. 2025 Feb 1;16(1):1254. doi: 10.1038/s41467-025-56443-3.
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 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 (BiNa)TiO-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. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm 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.
高熵策略已成为一种普遍采用的方法，用于提高弛豫铁电体的电容储能性能，以应用于先进的电气和电子系统。然而，由于广泛的成分空间，探索高性能的高熵体系面临挑战。在此，借助机器学习筛选，我们在一种无铅高熵弛豫铁电陶瓷中展示了20.7 J/cm³的高储能密度以及86%的高效率。基于有限的已报道实验数据，开发了一种带有关键描述符的随机森林回归模型，以预测和筛选高熵体系的元素及化学成分。经过基础实验，确定了一种基于(BiNa)TiO₃的高熵弛豫铁电体，其具有细晶粒、弱耦合和小尺寸极性团簇的特征。这导致了近线性的极化行为和95 kV/mm的超高击穿场强。此外，这种高熵弛豫铁电体在约27 ns的放电速率下呈现出7.7 J/cm³的高放电能量密度，以及优异的温度和疲劳稳定性。我们的结果展示了用于高效探索高性能高熵弛豫铁电体的数据驱动模型，证明了机器学习在开发弛豫铁电体方面的潜力。
