ダウンロード数: 198
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
---|---|---|---|---|
s41467-018-04940-z.pdf | 1.03 MB | Adobe PDF | 見る/開く |
タイトル: | Materials informatics for self-assembly of functionalized organic precursors on metal surfaces |
著者: | Packwood, Daniel M. Hitosugi, Taro |
著者名の別形: | パックウッド, ダニエル 一杉, 太郎 |
キーワード: | Computational methods Molecular self-assembly Statistics |
発行日: | 25-Jun-2018 |
出版者: | Springer Nature |
誌名: | Nature Communications |
巻: | 9 |
論文番号: | 2469 |
抄録: | Bottom-up fabrication via on-surface molecular self-assembly is a way to create defect-free, low-dimensional nanomaterials. For bottom-up fabrication to succeed, precursor molecules which correctly assemble into the target structure must be first identified. Here we present an informatics technique which connects self-assembled structures with particular chemical properties of the precursor molecules. Application of this method produces a visual output (a dendrogram) that functions much like the periodic table, but whereas the periodic table puts atoms into categories according to the way in which they bond to each other, the dendrogram put molecules into categories according to the way in which they arrange in a self-assembled structure. By applying this method to the case of functionalized bianthracene precursors adsorbed to copper(111), we identify the functional groups needed to assemble one-dimensional chains, two-dimensional tilings, and other shapes. This methodology can therefore help to identify appropriate precursor molecules for forming target nanomaterials via bottom-up fabrication. |
記述: | 「教師なし機械学習」を用いてナノ材料開発に必要なガイドラインを作ることに成功しました. 京都大学プレスリリース. 2018-08-03. |
著作権等: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2018. |
URI: | http://hdl.handle.net/2433/232639 |
DOI(出版社版): | 10.1038/s41467-018-04940-z |
PubMed ID: | 29941973 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2018-08-03-0 |
出現コレクション: | 学術雑誌掲載論文等 |
このリポジトリに保管されているアイテムはすべて著作権により保護されています。