Using Feature-Assisted Machine Learning Algorithms to Boost Polarity in Lead-Free Multicomponent Niobate Alloys for High-Performance Ferroelectrics

To expand the unchartered materials space of lead-free ferroelectrics for smart digital technologies, tuning their compositional complexity via multicomponent alloying allows access to enhanced polar properties. The role of isovalent A-site in binary potassium niobate alloys, (K,A)NbO₃ using first-principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO₃ are considered and their mixing thermodynamics and associated polar properties are examined. To establish structure-property design rules for high-performance ferroelectrics, the sure independence screening sparsifying operator (SISSO) method is employed to extract key features to explain the A-site driven polarization in (K,A)NbO₃ . Using a new metric of agreement via feature-assisted regression and classification, the SISSO model is further extended to predict A-site driven polarization in multicomponent systems as a function of alloy composition, reducing the prediction errors to less than 1%. With the machine learning model outlined in this work, a polarity-composition map is established to aid the development of new multicomponent lead-free polar oxides which can offer up to 25% boosting in A-site driven polarization and achieving more than 150% of the total polarization in pristine KNbO₃ . This study offers a design-based rational route to develop lead-free multicomponent ferroelectric oxides for niche information technologies.