Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39893180.
- Also identified by DOI 10.1038/s41467-025-56443-3 and PMC identifier 11787375.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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 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.