Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane
- Corresponding author: Qiao Zhiwei, zqiao@gzhu.edu.cn † These authors contributed equally to this work.
Citation:
Cai Chengzhi, Li Lifeng, Deng Xiaomei, Li Shuhua, Liang Hong, Qiao Zhiwei. Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane[J]. Acta Chimica Sinica,
;2020, 78(5): 427-436.
doi:
10.6023/A20030065
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(a) SC1/(C2+C3)-NC1; (b) NC1-LCD; (c) SC1/(C2+C3)-LCD; (d) SC2/(C1+C3)-NC2; (e) NC2-LCD; (f) SC2/(C1+C3)-LCD; (g) NC1-SC1/(C2+C3), LCD; (h) NC2-SC1/(C2+C3), LCD. The color represents the value of TSN. Each figure contains the data of 31399 hMOFs
The color represents the value of the TSN. The figure contains the data of 31399 hMOFs
(a) RF, (b) BPNN, (c) DT, (d) SVM. The color represents the number of MOFs
(a) Relative importance of the six descriptors for NC1, NC2, NC3. The color from yellow to red represents the relative importance; (b) Design paths for optimal MOFs. The optimal and suboptimal routes are highlighted in red and blue, respectively