Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning

: The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics. The ability to rapidly and accurately predict the piezoelectric coefficient ( d 33 ) 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 d 33 of potassium– sodium–niobate ((K,Na)NbO 3 , 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 d 33 out of all 27 possible features for the piezoelectric ceramics. These four features contribute greatly to regression learning for predicting d 33 and classification learning for distinguishing polymorphic phase boundary (PPB). The ML method developed in this work exhibits a high accuracy in predicting d 33 of the piezoelectric ceramics. An example of KNN combined with 6 mol% LiNbO 3 demonstrates d 33 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 d 33 , which is expected to be widely used in other functional materials.