Interpretable and uncertainty-informed machine learning to accelerate the design and discovery of lead-free piezoceramics with large piezoelectric constant

Abstract

Potassium sodium niobate (KNN)-based ceramics are promising alternatives to lead-containing piezoelectric materials. However, the vast design space, characterized by multiple dopant choices and variable content, presents a considerable challenge in the chemical modification of KNN compositions to improve their piezoelectric performance. In recent years, the rapid advance of machine learning (ML) techniques has facilitated expedited materials design and discovery with deeply sought insights into the materials. In this study, we constructed an interpretable and uncertainty-informed ML framework to optimize the piezoelectric coefficient d₃₃ of a KNN-based lead-free system. We identified and analyzed the influential features for the d₃₃ prediction and conducted three experimental iterations based on the uncertainty-informed predictions obtained from the Monte Carlo dropout (MCDropout). Promising KNN compositions exhibiting large d₃₃ values over 300 pC N⁻¹ were located and synthesized. Furthermore, the MCDropout markedly reduced the computational cost by 33% compared to the commonly used bootstrap method for uncertainty assessment. This study exhibits an ML framework with enhanced interpretability and search efficiency for optimizing the crucial piezoelectric properties of piezoceramics. The application scope of the utilized methods can be extended to various materials with tailored properties.