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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...

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Autores principales: Oh, Seung‐Hyun Victor, Hwang, Woohyun, Kim, Kwangrae, Lee, Ji‐Hwan, Soon, Aloysius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
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.
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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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