Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jeong Min | - |
dc.contributor.author | Ha, Taejun | - |
dc.contributor.author | Lee, Joonho | - |
dc.contributor.author | Lee, Young-Su | - |
dc.contributor.author | Shim, Jae-Hyeok | - |
dc.date.accessioned | 2024-01-19T10:03:13Z | - |
dc.date.available | 2024-01-19T10:03:13Z | - |
dc.date.created | 2022-07-21 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 1598-9623 | - |
dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/113989 | - |
dc.description.abstract | Pressure-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.language | English | - |
dc.publisher | 대한금속·재료학회 | - |
dc.title | Prediction of Pressure-Composition-Temperature Curves of AB(2)-Type Hydrogen Storage Alloys by Machine Learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s12540-022-01262-0 | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | Metals and Materials International, v.29, no.3, pp.861 - 869 | - |
dc.citation.title | Metals and Materials International | - |
dc.citation.volume | 29 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 861 | - |
dc.citation.endPage | 869 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.identifier.wosid | 000822473700001 | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | METAL-HYDRIDES | - |
dc.subject.keywordPlus | MN | - |
dc.subject.keywordPlus | TI | - |
dc.subject.keywordAuthor | Hydrogen storage alloy | - |
dc.subject.keywordAuthor | Hydrogen sorption | - |
dc.subject.keywordAuthor | Pressure-composition-temperature curve | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Deep neural network | - |
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