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Quantifying disorder one atom at a time using an interpretable graph neural network paradigm
Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric enco...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328988/ https://www.ncbi.nlm.nih.gov/pubmed/37419927 http://dx.doi.org/10.1038/s41467-023-39755-0 |
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author | Chapman, James Hsu, Tim Chen, Xiao Heo, Tae Wook Wood, Brandon C. |
author_facet | Chapman, James Hsu, Tim Chen, Xiao Heo, Tae Wook Wood, Brandon C. |
author_sort | Chapman, James |
collection | PubMed |
description | Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena. |
format | Online Article Text |
id | pubmed-10328988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103289882023-07-09 Quantifying disorder one atom at a time using an interpretable graph neural network paradigm Chapman, James Hsu, Tim Chen, Xiao Heo, Tae Wook Wood, Brandon C. Nat Commun Article Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena. Nature Publishing Group UK 2023-07-07 /pmc/articles/PMC10328988/ /pubmed/37419927 http://dx.doi.org/10.1038/s41467-023-39755-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chapman, James Hsu, Tim Chen, Xiao Heo, Tae Wook Wood, Brandon C. Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title | Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title_full | Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title_fullStr | Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title_full_unstemmed | Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title_short | Quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
title_sort | quantifying disorder one atom at a time using an interpretable graph neural network paradigm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328988/ https://www.ncbi.nlm.nih.gov/pubmed/37419927 http://dx.doi.org/10.1038/s41467-023-39755-0 |
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