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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)NbO(3) using first...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434731/ https://www.ncbi.nlm.nih.gov/pubmed/35253401 http://dx.doi.org/10.1002/advs.202104569 |
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author | Oh, Seung‐Hyun Victor Hwang, Woohyun Kim, Kwangrae Lee, Ji‐Hwan Soon, Aloysius |
author_facet | Oh, Seung‐Hyun Victor Hwang, Woohyun Kim, Kwangrae Lee, Ji‐Hwan Soon, Aloysius |
author_sort | Oh, Seung‐Hyun Victor |
collection | PubMed |
description | 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)NbO(3) using first‐principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO(3) 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)NbO(3). 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 KNbO(3). This study offers a design‐based rational route to develop lead‐free multicomponent ferroelectric oxides for niche information technologies. |
format | Online Article Text |
id | pubmed-9434731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94347312022-09-08 Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics Oh, Seung‐Hyun Victor Hwang, Woohyun Kim, Kwangrae Lee, Ji‐Hwan Soon, Aloysius Adv Sci (Weinh) Research Articles 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)NbO(3) using first‐principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO(3) 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)NbO(3). 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 KNbO(3). This study offers a design‐based rational route to develop lead‐free multicomponent ferroelectric oxides for niche information technologies. John Wiley and Sons Inc. 2022-03-06 /pmc/articles/PMC9434731/ /pubmed/35253401 http://dx.doi.org/10.1002/advs.202104569 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Oh, Seung‐Hyun Victor Hwang, Woohyun Kim, Kwangrae Lee, Ji‐Hwan Soon, Aloysius Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title | Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title_full | Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title_fullStr | Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title_full_unstemmed | Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title_short | Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics |
title_sort | using feature‐assisted machine learning algorithms to boost polarity in lead‐free multicomponent niobate alloys for high‐performance ferroelectrics |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434731/ https://www.ncbi.nlm.nih.gov/pubmed/35253401 http://dx.doi.org/10.1002/advs.202104569 |
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