About: Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

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scientific article published on 01 January 2020

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) научна статия (bg) artículu científicu espublizáu en xineru de 2020 (ast) article scientifique (publié 2020) (fr) articolo scientifico (pubblicato il 2020) (it) artigo científico (publicado na 2020) (pt) artikull shkencor i botuar më 01 janar 2020 (sq) scientific article published on 01 January 2020 (en) im Januar 2020 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована 1 січня 2020 (uk)
publication date	wds:Q92447014-F9B569BA-E31E-4E67-ABAA-BB2E74879ACB
publication date	2020-01-01 00:00:00Z (xsd:dateTime)
cites work	wds:Q92447014-293D661D-9A37-4827-955B-49BA9FF47F94 wds:Q92447014-38E84242-5119-407F-A7BE-D9AC66D472A2 wds:Q92447014-9E0C8C5D-331F-4895-8803-A425A9034184 wds:Q92447014-B469192C-39C3-49E6-A391-D30B68F8883A wds:Q92447014-BD9FA17F-C8B8-4399-A5C3-027043EE6F42 wds:Q92447014-D493791C-3589-437D-BD0B-ECE3F4B8B57B wds:Q92447014-F9087496-DF9A-43DD-8863-4E5D704EE49F
cites work	Observations of intergranular stress corrosion cracking in a grain-mapped polycrystal. Hydrogen Embrittlement of Metals: Atomic hydrogen from a variety of sources reduces the ductility of many metals Predicting phase behavior of grain boundaries with evolutionary search and machine learning. Grain boundary decohesion by impurity segregation in a nickel-sulfur system. First principles determination of the effects of phosphorus and boron on iron grain boundary cohesion Origin of the bismuth-induced decohesion of nickel and copper grain boundaries Partitioning of Interstitial Segregants during Decohesion: A DFT Case Study of the Σ3 Symmetric Tilt Grain Boundary in Ferritic Steel
author name string	wds:Q92447014-284A76E4-B4C1-4B91-A849-E03A6FF845A7 wds:Q92447014-33EE6422-7DEC-41D8-9347-4BB627FF7ADF wds:Q92447014-3C43F5B8-935E-431B-B906-293E73473DC5 wds:Q92447014-6652BAFA-4F50-4336-B9B3-2439992F2D46 wds:Q92447014-7803A4B4-FF17-4ED9-8B6A-0CF9274ECA97 wds:Q92447014-C6D79AF7-EA3A-43FD-88FC-9747476B7B24 wds:Q92447014-DBC120AD-3EBA-4925-B848-A9FD343B6722
author name string	Wei Liu Yichun Xu Xiangyan Li Yu-Xuan Wang Yange Zhang Changsong Liu Kan-Ni He
rdfs:label	Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (en) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (nl) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (sq)
skos:prefLabel	Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (en) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (nl) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (sq)
name	Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (en) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (nl) Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (sq)
author	wds:Q92447014-FED8DA8D-D53F-407F-960B-6EF55B800BF1
author	Xuebang Wu
title	wds:Q92447014-308A9016-5E40-4822-A78E-A1E494DD38E1
title	Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects (en)
instance of	wds:Q92447014-BA5A3EB3-F04B-4812-A525-BE428281483E
instance of	scholarly article
main subject	wds:Q92447014-5849DF07-4183-4380-98C9-AC2EEE6F0B99
main subject	machine learning
PubMed ID	wds:Q92447014-30B13BEF-9CF1-4736-A4BA-EDCD47CAF115
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/31906401
PubMed ID	31906401
published in	wds:Q92447014-131ABB5F-BDDD-4A6E-A3C9-55D71A93EAAC
published in	Materials
issue	wds:Q92447014-33DABD1D-0407-4073-9482-4B5E556EBC01
volume	wds:Q92447014-56BD0AC1-16AE-4F1B-8711-2A38010FFE31
issue	1
volume	13
DOI	wds:Q92447014-290E43C6-76B5-418C-ABED-F604B5BC4BD4
DOI	http://dx.doi.org/10.3390/MA13010179
DOI	10.3390/MA13010179
copyright license	wds:Q92447014-b0f5c7b9-784b-4963-8730-5d1e04f13527
copyright status	wds:Q92447014-9640877d-4023-4604-a9a5-cedc57451d33
copyright license	Creative Commons Attribution 4.0 International
copyright status	copyrighted
describes a project that uses	wds:Q92447014-C17F92F9-9F16-4EA9-B52A-A98658752A8A
describes a project that uses	machine learning
PMCID	wds:Q92447014-77DC0E81-A395-4D83-81BB-B0F4360DDEC3
PMCID	6981756
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q92447014

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