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dc.contributor.authorKim, Jeong Min-
dc.contributor.authorHa, Taejun-
dc.contributor.authorLee, Joonho-
dc.contributor.authorLee, Young-Su-
dc.contributor.authorShim, Jae-Hyeok-
dc.date.accessioned2024-01-19T10:03:13Z-
dc.date.available2024-01-19T10:03:13Z-
dc.date.created2022-07-21-
dc.date.issued2023-03-
dc.identifier.issn1598-9623-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/113989-
dc.description.abstractPressure-composition-temperature (PCT) curves for hydrogen absorption and desorption of AB(2)-type hydrogen storage alloys at arbitrary temperatures are predicted by three machine learning models such as random forest, K-nearest neighbor and deep neural network (DNN). Two data generation methods are adopted to increase the number of data points. A new form of the PCT curve functions is suggested to fit experimental data, which greatly helps improve the prediction accuracy. A van't Hoff type equation is used to generate unmeasured temperature data, which improves the model performance on the PCT behavior at various temperatures. The results indicate that a DNN is the best model for predicting the PCT behavior with a high average correlation value R-2 = 0.93070.-
dc.languageEnglish-
dc.publisher대한금속·재료학회-
dc.titlePrediction of Pressure-Composition-Temperature Curves of AB(2)-Type Hydrogen Storage Alloys by Machine Learning-
dc.typeArticle-
dc.identifier.doi10.1007/s12540-022-01262-0-
dc.description.journalClass1-
dc.identifier.bibliographicCitationMetals and Materials International, v.29, no.3, pp.861 - 869-
dc.citation.titleMetals and Materials International-
dc.citation.volume29-
dc.citation.number3-
dc.citation.startPage861-
dc.citation.endPage869-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.identifier.wosid000822473700001-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMetallurgy & Metallurgical Engineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaMetallurgy & Metallurgical Engineering-
dc.type.docTypeArticle-
dc.subject.keywordPlusMETAL-HYDRIDES-
dc.subject.keywordPlusMN-
dc.subject.keywordPlusTI-
dc.subject.keywordAuthorHydrogen storage alloy-
dc.subject.keywordAuthorHydrogen sorption-
dc.subject.keywordAuthorPressure-composition-temperature curve-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorDeep neural network-
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