{"created":"2023-05-15T14:56:06.282463+00:00","id":76194,"links":{},"metadata":{"_buckets":{"deposit":"f2fa60fb-9185-4d26-93db-c4da8a217ffb"},"_deposit":{"created_by":1,"id":"76194","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"76194"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00076194","sets":["10:29"]},"author_link":["768546","768545","768549","768550","768548","768547"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2019-07-03","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"放射線グラフト電解質膜のイオン交換基密度や基材高分子の物性値を説明変数として、プロトン導電率を予測する人工ニューラルネットワーク(ANN)モデルを構築した。基材高分子の物性値は、文献参照、実測定、量子化学計算、によって収集した。プロトン導電率については、8種類の異なる基材高分子から作製された電解質膜のデータを既報から抽出して用いた。ANNモデルはプロトン導電率を高精度（決定係数 = 0.97）で予測することができた。ANNモデルを解析することで、イオン交換基密度だけでなく、基材高分子の分極モーメントや最低空軌道のエネルギーといった物性値が導電率の予測精度に寄与することがわかった。","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"第56回アイソトープ・放射線研究発表会","subitem_description_type":"Other"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"澤田, 真一"}],"nameIdentifiers":[{"nameIdentifier":"768545","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"田中, 健一"}],"nameIdentifiers":[{"nameIdentifier":"768546","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"船津, 公人"}],"nameIdentifiers":[{"nameIdentifier":"768547","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"前川, 康成"}],"nameIdentifiers":[{"nameIdentifier":"768548","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sawada, Shinichi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"768549","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Maekawa, Yasunari","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"768550","nameIdentifierScheme":"WEKO"}]}]},"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":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"機械学習による放射線グラフト電解質膜のプロトン導電率予測","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習による放射線グラフト電解質膜のプロトン導電率予測"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-07-03"},"publish_date":"2019-07-03","publish_status":"0","recid":"76194","relation_version_is_last":true,"title":["機械学習による放射線グラフト電解質膜のプロトン導電率予測"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-16T00:31:52.147514+00:00"}