{"created":"2023-06-20T13:53:40.615375+00:00","id":18,"links":{},"metadata":{"_buckets":{"deposit":"5211afb3-cb70-4e23-8457-86d8efdb0db9"},"_deposit":{"created_by":7,"id":"18","owners":[7],"pid":{"revision_id":0,"type":"depid","value":"18"},"status":"published"},"_oai":{"id":"oai:sangitan.repo.nii.ac.jp:00000018","sets":["1:7"]},"author_link":["13","14","15"],"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"１","bibliographicPageEnd":"51","bibliographicPageStart":"49","bibliographicVolumeNumber":"55","bibliographic_titles":[{"bibliographic_title":"産業技術短期大学誌"},{"bibliographic_title":"BULLETIN OF COLLEGE OF INDUSTRIAL TECHNOLOGY","bibliographic_titleLang":"en"}]}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The inter-atomic potential based on artificial neural network (ANN) is very promising tool for atomic modeling. Using high quality training set constructed by First principles calculation based on Density functional theory (DFT), ANN potential would become sophisticated replica of DFT. However, the transferability of ANN potential depends on the DFT training dataset. In this study, we perform molecular dynamics (MD) calculations for molten iron, which is not directly included in the training data. We also examine the results to discuss the applicability of the constructed potential.","subitem_description_type":"Abstract"}]},"item_10002_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0916-3727","subitem_source_identifier_type":"ISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森, 英喜"},{"creatorName":"モリ, ヒデキ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥村, 雅彦"},{"creatorName":"オクムラ, マサヒコ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"板倉, 充洋"},{"creatorName":"イタクラ, ミツヒロ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-03-10"}],"displaytype":"simple","filename":"vol55_08_研究ノート_森 英喜_p49-51.pdf","filesize":[{"value":"485.9 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"vol55_08_研究ノート_森 英喜_p49-51","url":"https://sangitan.repo.nii.ac.jp/record/18/files/vol55_08_研究ノート_森 英喜_p49-51.pdf"},"version_id":"67737a7b-2b7d-4536-92e5-d5c73f6873b1"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"研究ノート","subitem_subject_scheme":"Other"},{"subitem_subject":"Machine Learning potential","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"first principles calculation","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"molten iron","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"08_機械学習ポテンシャルによる溶融鉄構造再現性の検討","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"08_機械学習ポテンシャルによる溶融鉄構造再現性の検討"},{"subitem_title":"Consideration of Transferability of Molten Iron of Machine Learning Potentials","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"7","path":["7"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-10"},"publish_date":"2022-03-10","publish_status":"0","recid":"18","relation_version_is_last":true,"title":["08_機械学習ポテンシャルによる溶融鉄構造再現性の検討"],"weko_creator_id":"7","weko_shared_id":-1},"updated":"2023-06-20T13:56:31.070619+00:00"}