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

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

Funding Information:
The authors gratefully acknowledge support by grant from National Research Foundation of Korea under the Material Convergence Innovation Technology Development Program (2020M3D1A2102913), Ministry of Science, and ICT under the Creative Materials Discovery Program (2018M3D1A1058536), Computational resources have been kindly provided by the KISTI Supercomputing Center (KSC‐2019‐CRE‐0174) and the Australian National Computational Infrastructure (NCI).

Funding Information:
The authors gratefully acknowledge support by grant from National Research Foundation of Korea under the Material Convergence Innovation Technology Development Program (2020M3D1A2102913), Ministry of Science, and ICT under the Creative Materials Discovery Program (2018M3D1A1058536), Computational resources have been kindly provided by the KISTI Supercomputing Center (KSC-2019-CRE-0174) and the Australian National Computational Infrastructure (NCI).

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

All Science Journal Classification (ASJC) codes

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

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