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

ABSTRACT: 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 f

SUBMITTER: Oh SV 

PROVIDER: S-EPMC9434731 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature