Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool for finding correlations but when used to look at re...

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Autores principales:	Conrad W. Rosenbrock, Eric R. Homer, Gábor Csányi, Gus L. W. Hart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b71a7a9a5df64438a53ef378aa2fd64e
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spelling	oai:doaj.org-article:b71a7a9a5df64438a53ef378aa2fd64e2021-12-02T12:30:32ZDiscovering the building blocks of atomic systems using machine learning: application to grain boundaries10.1038/s41524-017-0027-x2057-3960https://doaj.org/article/b71a7a9a5df64438a53ef378aa2fd64e2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-017-0027-xhttps://doaj.org/toc/2057-3960Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool for finding correlations but when used to look at real-word systems, the complexity of the models often limits the amount of information that can be extracted about the underlying physics. An international team of researchers led by Conrad Rosenbrock from Brigham Young University now present a machine learning-based approach for modelling atomic systems that can provide insight into the physical building blocks that influence them. They demonstrate the power of their approach by examining the predictive performance of several machine learning models, providing connections between the structure and behaviour of grain boundaries in crystalline materials, which could be extended to other systems that involve local structural changes.Conrad W. RosenbrockEric R. HomerGábor CsányiGus L. W. HartNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 3, Iss 1, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Conrad W. Rosenbrock Eric R. Homer Gábor Csányi Gus L. W. Hart Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
description	Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool for finding correlations but when used to look at real-word systems, the complexity of the models often limits the amount of information that can be extracted about the underlying physics. An international team of researchers led by Conrad Rosenbrock from Brigham Young University now present a machine learning-based approach for modelling atomic systems that can provide insight into the physical building blocks that influence them. They demonstrate the power of their approach by examining the predictive performance of several machine learning models, providing connections between the structure and behaviour of grain boundaries in crystalline materials, which could be extended to other systems that involve local structural changes.
format	article
author	Conrad W. Rosenbrock Eric R. Homer Gábor Csányi Gus L. W. Hart
author_facet	Conrad W. Rosenbrock Eric R. Homer Gábor Csányi Gus L. W. Hart
author_sort	Conrad W. Rosenbrock
title	Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
title_short	Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
title_full	Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
title_fullStr	Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
title_full_unstemmed	Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
title_sort	discovering the building blocks of atomic systems using machine learning: application to grain boundaries
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b71a7a9a5df64438a53ef378aa2fd64e
work_keys_str_mv	AT conradwrosenbrock discoveringthebuildingblocksofatomicsystemsusingmachinelearningapplicationtograinboundaries AT ericrhomer discoveringthebuildingblocksofatomicsystemsusingmachinelearningapplicationtograinboundaries AT gaborcsanyi discoveringthebuildingblocksofatomicsystemsusingmachinelearningapplicationtograinboundaries AT guslwhart discoveringthebuildingblocksofatomicsystemsusingmachinelearningapplicationtograinboundaries
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Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

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