{"id":212171,"updated":"2025-01-19T17:34:47.101388+00:00","links":{},"created":"2025-01-19T01:13:11.154253+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212171","sets":["1164:2735:10526:10644"]},"path":["10644"],"owner":"44499","recid":"212171","title":["深層学習による効率的な高精度量子化学計算結果予測手法の開発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-20"},"_buckets":{"deposit":"f7430309-553b-49c9-848e-cb05a48ab205"},"_deposit":{"id":"212171","pid":{"type":"depid","value":"212171","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層学習による効率的な高精度量子化学計算結果予測手法の開発","author_link":["540675","540673","540674"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習による効率的な高精度量子化学計算結果予測手法の開発"}]},"item_type_id":"4","publish_date":"2021-07-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学情報理工学院情報工学系"},{"subitem_text_value":"東京工業大学物質・情報卓越教育院"},{"subitem_text_value":"東京工業大学情報理工学院情報工学系"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/212171/files/IPSJ-MPS21134012.pdf","label":"IPSJ-MPS21134012.pdf"},"date":[{"dateType":"Available","dateValue":"2023-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS21134012.pdf","filesize":[{"value":"897.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"12473950-63f3-4831-88c7-ee07123f52e3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Wan, Mingda"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"安尾, 信明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"関嶋, 政和"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"分子の化学的性質を予測することは，量子化学計算の主な目的の 1 つである．密度汎関数法（DFT）は，G4 (MP2) のような複雑な計算手法に比べて高速な計算手法であるが，計算精度は十分ではない．本研究では，DFT と G4 (MP2) で計算された原子化エネルギーの差を，深層学習を用いた予測によって補正可能であることを示した．QM9 データセットを用いた実験では，1 万個の学習データにより，テストデータの平均絶対誤差を 1kcal・mol-1 以下にすることができ，G4 (MP2) レベルの計算と同等の結果が得ることが出来た．この結果は，DFT と機械学習を用いた補正を組み合わせることで，計算精度では十分でない結果を補正可能である可能性を示唆している．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2021-MPS-134"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}