Prediction of Specific Surface Area of Metal-Organic Frameworks by Graph Kernels

• Item type: SIG Technical Reports(1)
• 公開日: 2023-07-17
• タイトル: Prediction of Specific Surface Area of Metal-Organic Frameworks by Graph Kernels
• タイトル / 言語: en
• タイトル: Prediction of Specific Surface Area of Metal-Organic Frameworks by Graph Kernels
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Presently with Computer Centre, Gakushuin Univ.
• 著者所属: Presently with Computer Centre, Gakushuin Univ.
• 著者所属: Presently with SAKAI CHEMICAL INDUSTRY CO., LTD.
• 著者所属: Presently with Graduate School of Information Science, University of Hyogo
• 著者所属(英): en
• 著者所属(英): Presently with Computer Centre, Gakushuin Univ.
• 著者所属(英): en
• 著者所属(英): Presently with Computer Centre, Gakushuin Univ.
• 著者所属(英): en
• 著者所属(英): Presently with SAKAI CHEMICAL INDUSTRY CO., LTD.
• 著者所属(英): en
• 著者所属(英): Presently with Graduate School of Information Science, University of Hyogo
• 著者名: Yu, Morikawa; Kilho, Shin; Masataka, Kubouchi; Hiroaki, Ohshima
• 著者名(英): Yu, Morikawa; Kilho, Shin; Masataka, Kubouchi; Hiroaki, Ohshima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Metal-organic frameworks (MOFs) are coordination networks of metal ions or clusters bonded by organic ligands, and are expected to have a significantly wide application range due to their tunable micro-porosity and unprecedented surface area. While the diverse range of choices on combinations of metals and organic ligands is the origin of the tunable micro-porosity, it also makes deployment of optimal combinations difficult. The aim of this research is to propose a method to search candidates of optimal combinations leveraging machine learning techniques without actual synthesis of MOFs and as a result to make development of MOFs more effective and more efficient. In order to incorporate the structural features of organic ligands into investigation, this paper leverages graph kernels: the structural features should be represented faithfully by graphs of atoms, and graph kernels evaluate the similarity between graphs. Furthermore, we define a unified kernel to evaluate the similarity between MOFs, which is a weighted sum of an RBF kernel to evaluate the similarity of metals and the said graph kernel. With this unified kernel and the support vector machine classification algorithm, we develop a model that predicts whether a given combination of a metal and an organic ligand can result in an MOF with high specific surface area. Through the experiments with MOF data retrieved from the CoRE MOF dataset, we have verified that our proposed method can generate models with good predictive performance for six of the seven graph kernels well known in the literature.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Metal-organic frameworks (MOFs) are coordination networks of metal ions or clusters bonded by organic ligands, and are expected to have a significantly wide application range due to their tunable micro-porosity and unprecedented surface area. While the diverse range of choices on combinations of metals and organic ligands is the origin of the tunable micro-porosity, it also makes deployment of optimal combinations difficult. The aim of this research is to propose a method to search candidates of optimal combinations leveraging machine learning techniques without actual synthesis of MOFs and as a result to make development of MOFs more effective and more efficient. In order to incorporate the structural features of organic ligands into investigation, this paper leverages graph kernels: the structural features should be represented faithfully by graphs of atoms, and graph kernels evaluate the similarity between graphs. Furthermore, we define a unified kernel to evaluate the similarity between MOFs, which is a weighted sum of an RBF kernel to evaluate the similarity of metals and the said graph kernel. With this unified kernel and the support vector machine classification algorithm, we develop a model that predicts whether a given combination of a metal and an organic ligand can result in an MOF with high specific surface area. Through the experiments with MOF data retrieved from the CoRE MOF dataset, we have verified that our proposed method can generate models with good predictive performance for six of the seven graph kernels well known in the literature.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2023-MPS-144, 号 12, p. 1-4, 発行日 2023-07-17
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会