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