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