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Material research from the viewpoint of functional motifs
As early as 2001, the need for the ‘functional motif theory’ was pointed out, to assist the rational design of functional materials. The properties of materials are determined by their functional motifs and how they are arranged in the materials. Uncovering functional motifs and their arrangements i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9379984/ https://www.ncbi.nlm.nih.gov/pubmed/35983369 http://dx.doi.org/10.1093/nsr/nwac017 |
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author | Jiang, Xiao-Ming Deng, Shuiquan Whangbo, Myung-Hwan Guo, Guo-Cong |
author_facet | Jiang, Xiao-Ming Deng, Shuiquan Whangbo, Myung-Hwan Guo, Guo-Cong |
author_sort | Jiang, Xiao-Ming |
collection | PubMed |
description | As early as 2001, the need for the ‘functional motif theory’ was pointed out, to assist the rational design of functional materials. The properties of materials are determined by their functional motifs and how they are arranged in the materials. Uncovering functional motifs and their arrangements is crucial in understanding the properties of materials and rationally designing new materials of desired properties. The functional motifs of materials are the critical microstructural units (e.g. constituent components and building blocks) that play a decisive role in generating certain material functions, and can not be replaced with other structural units without the loss, or significant suppression, of relevant functions. The role of functional motifs and their arrangement in materials, with representative examples, is presented. The microscopic structures of these examples can be classified into six types on a length scale smaller than ∼10 nm with maximum subatomic resolution, i.e. crystal, magnetic, aperiodic, defect, local and electronic structures. Functional motif analysis can be employed in the function-oriented design of materials, as elucidated by taking infrared non-linear optical materials as an example. Machine learning is more efficient in predicting material properties and screening materials with high efficiency than high-throughput experimentation and high-throughput calculations. In order to extract functional motifs and find their quantitative relationships, the development of sufficiently reliable databases for material structures and properties is imperative. |
format | Online Article Text |
id | pubmed-9379984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93799842022-08-17 Material research from the viewpoint of functional motifs Jiang, Xiao-Ming Deng, Shuiquan Whangbo, Myung-Hwan Guo, Guo-Cong Natl Sci Rev Review As early as 2001, the need for the ‘functional motif theory’ was pointed out, to assist the rational design of functional materials. The properties of materials are determined by their functional motifs and how they are arranged in the materials. Uncovering functional motifs and their arrangements is crucial in understanding the properties of materials and rationally designing new materials of desired properties. The functional motifs of materials are the critical microstructural units (e.g. constituent components and building blocks) that play a decisive role in generating certain material functions, and can not be replaced with other structural units without the loss, or significant suppression, of relevant functions. The role of functional motifs and their arrangement in materials, with representative examples, is presented. The microscopic structures of these examples can be classified into six types on a length scale smaller than ∼10 nm with maximum subatomic resolution, i.e. crystal, magnetic, aperiodic, defect, local and electronic structures. Functional motif analysis can be employed in the function-oriented design of materials, as elucidated by taking infrared non-linear optical materials as an example. Machine learning is more efficient in predicting material properties and screening materials with high efficiency than high-throughput experimentation and high-throughput calculations. In order to extract functional motifs and find their quantitative relationships, the development of sufficiently reliable databases for material structures and properties is imperative. Oxford University Press 2022-02-12 /pmc/articles/PMC9379984/ /pubmed/35983369 http://dx.doi.org/10.1093/nsr/nwac017 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Jiang, Xiao-Ming Deng, Shuiquan Whangbo, Myung-Hwan Guo, Guo-Cong Material research from the viewpoint of functional motifs |
title | Material research from the viewpoint of functional motifs |
title_full | Material research from the viewpoint of functional motifs |
title_fullStr | Material research from the viewpoint of functional motifs |
title_full_unstemmed | Material research from the viewpoint of functional motifs |
title_short | Material research from the viewpoint of functional motifs |
title_sort | material research from the viewpoint of functional motifs |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9379984/ https://www.ncbi.nlm.nih.gov/pubmed/35983369 http://dx.doi.org/10.1093/nsr/nwac017 |
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