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

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^-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_0.5Na_0.5)TiO₃-based high-entropy relaxor char

SUBMITTER: Wang X 

PROVIDER: S-EPMC11787375 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature