Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient

Heng Hu, Miaomiao Huang, Bin Wang, Didi Zhang, Tao Tan, Kang Yan, Dawei Wu

研究成果: Article同行評審

摘要

The piezoelectricity of piezoceramics considerably depends on the doped compositions. However, the conventional trial-and-error approach to composition design is time-consuming and inefficient when searching for high-performance piezoceramics with various dopants. In this study, we introduce a data-driven framework to accelerate the design and discovery of lead-free (K,Na)NbO3 (KNN) materials with enhanced piezoelectric performance. The proposed framework integrates machine learning with feature engineering, surrogate-based optimisation, and experimentation in an active learning loop. Using feature engineering techniques, we demonstrated that the piezoelectric coefficient d33 of KNN materials can be predicted based on a set of elemental and atomic properties. We evaluated six machine learning models and found that support vector regression with a radial basis function kernel achieved high accuracy in predicting the d33 values of KNN ceramics. After three iterations of experimentation and optimisation, we identified an optimal composition with a relatively high d33 of ∼353 pC/N from thousands of possible compositions using a pure exploitation strategy. This study demonstrates an efficient and systematic approach to the composition design of KNN-based piezoceramics, which can be applied to investigate the diverse functional properties of other materials.

原文English
頁(從 - 到)54536-54546
頁數11
期刊Ceramics International
50
發行號24
DOIs
出版狀態Published - 15 12月 2024

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