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

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 (Bi 0.5 Na 0.5 )TiO 3 -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. Designing high-performance high-entropy dielectric relaxors is a challenge. The authors explore a sodium bismuth titanate-based high-entropy near-linear relaxor with giant energy-storage capacity assisted by machine learning.