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

Seung Hyun Victor Oh, Woohyun Hwang, Kwangrae Kim, Ji Hwan Lee, Aloysius Soon

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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 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.

Original languageEnglish
Article number2104569
JournalAdvanced Science
Volume9
Issue number13
DOIs
Publication statusPublished - 2022 May 5

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • General Chemical Engineering
  • General Materials Science
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • General Engineering
  • General Physics and Astronomy

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