Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a miss...

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Autores principales:	Bharat Medasani, Anthony Gamst, Hong Ding, Wei Chen, Kristin A Persson, Mark Asta, Andrew Canning, Maciej Haranczyk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/a32c20d9819043b39e1bf57b270fa447
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spelling	oai:doaj.org-article:a32c20d9819043b39e1bf57b270fa4472021-12-02T12:30:49ZPredicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning10.1038/s41524-016-0001-z2057-3960https://doaj.org/article/a32c20d9819043b39e1bf57b270fa4472016-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-016-0001-zhttps://doaj.org/toc/2057-3960Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys.Bharat MedasaniAnthony GamstHong DingWei ChenKristin A PerssonMark AstaAndrew CanningMaciej HaranczykNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 2, Iss 1, Pp 1-10 (2016)
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 Bharat Medasani Anthony Gamst Hong Ding Wei Chen Kristin A Persson Mark Asta Andrew Canning Maciej Haranczyk Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
description	Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys.
format	article
author	Bharat Medasani Anthony Gamst Hong Ding Wei Chen Kristin A Persson Mark Asta Andrew Canning Maciej Haranczyk
author_facet	Bharat Medasani Anthony Gamst Hong Ding Wei Chen Kristin A Persson Mark Asta Andrew Canning Maciej Haranczyk
author_sort	Bharat Medasani
title	Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
title_short	Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
title_full	Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
title_fullStr	Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
title_full_unstemmed	Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
title_sort	predicting defect behavior in b2 intermetallics by merging ab initio modeling and machine learning
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/a32c20d9819043b39e1bf57b270fa447
work_keys_str_mv	AT bharatmedasani predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT anthonygamst predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT hongding predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT weichen predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT kristinapersson predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT markasta predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT andrewcanning predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning AT maciejharanczyk predictingdefectbehaviorinb2intermetallicsbymergingabinitiomodelingandmachinelearning
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Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

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