Machine learning accelerated discovery of high transmittance in (K0.5Na0.5)NbO3-based ceramics

High optical transmittance (T%) has always been an important indicator of transparent-ferroelectric ceramics for optoelectronic coupling. However, the pathway of pursuing high transparency has been at the experimental trial-and-error stage over the past decades, manifesting major drawbacks of being time-consuming and resource-wasting. The present work introduces a machine learning (ML) accelerated development of highly transparent-ferroelectrics by taking potassium-sodium niobate (KNN)-based ceramics as the model material. It is highlighted that by using a small data set of 118 sample data and four key features, we predict the T% of un-synthesized KNN-based ceramics and evaluate the importance of key features. Meanwhile, the screened (K0.5Na0.5)0.956Tb0.004Ba0.04NbO3 ceramics were successfully realized by the conventional solid-state synthesis, and the experimental measured T% is in full agreement with the predicted results, exhibiting a satisfactory high T% of ~78% at 800 nm. In addition, ML is also used to explore the best experimental parameters, and the prediction results of T% are particularly sensitive to changes in sintering temperature (ST). Eventually, the predicted optimal ST is highly consistent with the experimental one. This study constructs a new avenue for exploring high T% ferroelectric KNN ceramics based on ML, ascertaining optimal process parameters, and guiding the development of other transparent-ferroelectrics in optoelectronic fields.