期刊論文
| 學年 | 109 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2021-02-01 |
| 作品名稱 | 深度學習結合分子結構準確預測界面張力 |
| 作品名稱（其他語言） | Molecular structure incorporated deep learning approach for the accurate interfacial tension predictions |
| 著者 | Yan-Ling Yang; Heng-Kwong Tsao; Yu-Jane Sheng |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Molecular Liquids 323, 114571 |
| 摘要 | Characterization of the interface of a two-phase system by interfacial tension (IFT) imposes a great impact on the chemical and environmental engineering. In this work, a deep neural network (DNN) approach was developed to estimate IFT of water-hydrocarbon and water-alcohol interfaces. The predictive power of this approach for IFT was found to be much more improved than those of the previously proposed empirical correlations, both qualitatively and quantitatively. The input vector of two-phase systems generally contains five parameters, including critical temperature, critical pressure, and density difference, in addition to temperature and pressure. In this approach, a line notation describing the molecular structure of chemical species was also taken as an input. The most accurate results with the root-mean-square error (RMSE) of 1.28 mN/m are acquired as all six parameters are included. However, our analyses show that density difference and molecular structure are much more important than the critical properties. As a result, the DNN approach with the input vector involving molecular structure, temperature, and pressure only is able to yield sufficiently accurate results (RMSE 1.71 mN/m), and can successfully depict the descending, ascending, and concave dependences of IFT on temperature. |
| 關鍵字 | Deep learning approach;Deep neural network;Molecular structure, interfacial tension;Water-organic fluid interfaces |
| 語言 | en |
| ISSN | 1873-3166 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120172 ) |