machine learning (ml), piezoelectric ceramics, piezoelectric properties, perovskite, regression learning, Clay industries. Ceramics. Glass, TP785-869

The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics. The ability to rapidly and accurately predict the piezoelectric coefficient (d33) is of much practical importance for exploring high-performance piezoelectric ceramics. In this work, a data-driven approach combining feature engineering, statistical learning, machine learning (ML), experimental design, and synthesis is trialed to investigate its accuracy in predicting d33 of potassium–sodium–niobate ((K,Na)NbO3, KNN)-based ceramics. The atomic radius (AR), valence electron distance (DV) (Schubert), Martynov–Batsanov electronegativity (EN-MB), and absolute electronegativity (EN) are summarized as the four most representative features in describing d33 out of all 27 possible features for the piezoelectric ceramics. These four features contribute greatly to regression learning for predicting d33 and classification learning for distinguishing polymorphic phase boundary (PPB). The ML method developed in this work exhibits a high accuracy in predicting d33 of the piezoelectric ceramics. An example of KNN combined with 6 mol% LiNbO3 demonstrates d33 of 184 pC/N, which is highly consistent with the predicted result. This work proposes a novel feature-oriented guideline for accelerating the design of piezoelectric ceramic systems with large d33, which is expected to be widely used in other functional materials.