Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane
Abstract
1. Introduction
2. Model and Methods
2.1. Molecular Model
2.2. Molecular Simulations
2.3. Machine Learning
3. Results and Discussion
3.1. Statistical Analysis
3.2. Machine Learning
3.3. SHAP Analysis
3.4. Top-Performing Metal–Organic Frameworks (MOFs)
3.5. Design Strategies of MOFs with High Performances
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Indicators | Algorithm | R2 | MAE | RMSE |
---|---|---|---|---|
D | RF | 0.934 | 0.151 | 0.247 |
LGBM | 0.954 | 0.127 | 0.207 | |
XGB | 0.957 | 0.119 | 0.199 | |
GBRT | 0.947 | 0.129 | 0.222 | |
S | RF | 0.887 | 0.155 | 0.264 |
LGBM | 0.931 | 0.127 | 0.207 | |
XGB | 0.928 | 0.123 | 0.210 | |
GBRT | 0.907 | 0.135 | 0.240 |
Gas Mixture i/j | CSD Cord | LCD [Å] | ϕ | PLD [Å] | ρ [kg/m3] | Di [cm2/s] | Dj [cm2/s] | Sdiff(i/j) |
---|---|---|---|---|---|---|---|---|
He/CH4 | ELUQIM04 | 2.92 | 0.04 | 2.44 | 1764.65 | 9.03 × 10−6 | 1.47 × 10−10 | 61,406.36 |
ELUQIM05 | 2.90 | 0.04 | 2.43 | 1773.83 | 8.42 × 10−6 | 1.76 × 10−10 | 47,815.36 | |
ELUQIM06 | 2.89 | 0.04 | 2.41 | 1779.43 | 9.74 × 10−6 | 8.11 × 10−10 | 12,015.02 | |
H2/CH4 | ELUQIM05 | 2.90 | 0.04 | 2.43 | 1773.83 | 7.08 × 10−6 | 1.76 × 10−10 | 40,173.07 |
ELUQIM04 | 2.92 | 0.04 | 2.44 | 1764.65 | 4.14 × 10−6 | 1.47 × 10−10 | 28,148.27 | |
FAPYEA04 | 2.47 | 0.00 | 2.40 | 1583.54 | 2.83 × 10−6 | 3.01 × 10−10 | 9397.11 | |
CO2/CH4 | XEKDUO | 2.98 | 0.02 | 2.75 | 1903.55 | 1.70 × 10−7 | 6.92 × 10−11 | 2463.01 |
ELUQIM05 | 2.90 | 0.04 | 2.43 | 1773.83 | 3.91 × 10−7 | 1.76 × 10−10 | 2220.35 | |
HIQPEE | 3.84 | 0.15 | 3.12 | 1440.14 | 1.24 × 10−6 | 5.69 × 10−10 | 2185.18 | |
O2/CH4 | FAPYEA04 | 2.47 | 0.00 | 2.40 | 1583.54 | 2.92 × 10−7 | 3.01 × 10−10 | 968.16 |
ELUQIM05 | 2.90 | 0.04 | 2.43 | 1773.83 | 1.58 × 10−7 | 1.76 × 10−10 | 898.78 | |
GUXQAS | 2.79 | 0.02 | 2.52 | 1598.28 | 1.16 × 10−7 | 1.30 × 10−10 | 893.53 | |
H2S/CH4 | GUXQAS | 2.79 | 0.02 | 2.52 | 1598.28 | 1.70 × 10−9 | 1.30 × 10−10 | 13.04 |
RUPZIM | 3.48 | 0.11 | 3.25 | 1549.49 | 1.51 × 10−7 | 1.39 × 10−8 | 10.87 | |
GUXPUL | 2.79 | 0.02 | 2.58 | 1595.02 | 1.18 × 10−9 | 1.10 × 10−10 | 10.70 | |
N2/CH4 | FAPYEA04 | 2.47 | 0.00 | 2.40 | 1583.54 | 1.11 × 10−7 | 3.01 × 10−10 | 369.05 |
PARFOF | 2.77 | 0.05 | 2.46 | 1541.02 | 4.78 × 10−7 | 2.16 × 10−9 | 221.18 | |
HIWXER01 | 3.29 | 0.13 | 2.76 | 2533.00 | 1.80 × 10−7 | 1.27 × 10−9 | 141.34 |
Gas Mixture (i/j) | NO. | CSD Cord | Metal Center | Organic Links | Top Structure | Sdiff(i/j) |
---|---|---|---|---|---|---|
H2/CH4 | a | GIRDUI | Co | MGFJDEHFNMWYBD | pcu | 7.93 |
GIRGUL | Co | MTAVBTGOXNGCJR | pcu | 643.48 | ||
b | CIMTAV | La | BVKZGUZCCUSVTD | llj | 4.58 | |
CIMTEZ | Nd | BVKZGUZCCUSVTD | llj | 12.97 | ||
c | EBELUU | NiZn | JEVCWSUVFOYBFI | fsc | 5.36 | |
EBEMEF | NiZn | JEVCWSUVFOYBFI | pts | 147.93 | ||
CO2/CH4 | d | CAJQEL | Cu | GRYHAGOZZMMYAO | scu | 0.31 |
CAJQIP | Cu | DUKMDOUQAIDJRW | scu | 2.24 | ||
e | HEBTEP | Zn | ABMFBCRYHDZLRD | lim | 21.93 | |
SETFUT | Cd | ABMFBCRYHDZLRD | lim | 1.22 | ||
H2S/CH4 | f | ISIKIF | Co | GEBVRXNOWAYDCP | dia | 16.07 |
ISIKOL | Co | GEBVRXNOWAYDCP | bbf | 0.97 |
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Guan, Y.; Huang, X.; Xu, F.; Wang, W.; Li, H.; Gong, L.; Zhao, Y.; Guo, S.; Liang, H.; Qiao, Z. Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane. Nanomaterials 2024, 14, 1074. https://doi.org/10.3390/nano14131074
Guan Y, Huang X, Xu F, Wang W, Li H, Gong L, Zhao Y, Guo S, Liang H, Qiao Z. Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane. Nanomaterials. 2024; 14(13):1074. https://doi.org/10.3390/nano14131074
Chicago/Turabian StyleGuan, Yafang, Xiaoshan Huang, Fangyi Xu, Wenfei Wang, Huilin Li, Lingtao Gong, Yue Zhao, Shuya Guo, Hong Liang, and Zhiwei Qiao. 2024. "Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane" Nanomaterials 14, no. 13: 1074. https://doi.org/10.3390/nano14131074
APA StyleGuan, Y., Huang, X., Xu, F., Wang, W., Li, H., Gong, L., Zhao, Y., Guo, S., Liang, H., & Qiao, Z. (2024). Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane. Nanomaterials, 14(13), 1074. https://doi.org/10.3390/nano14131074