Machine learning aided evaluation and design based on polymer membrane materials

Figures & data

Figure 1. Schematic diagram of the application process of machine learning methods in materials research.

Figure 2. A Robeson plot of the selectivity versus permeability of the CO₂/CH₄ isolation.

Figure 3. Scikit-learn usage diagram.

Figure 4. ：CO₂/methane gas separation performance of a series of polymer membranes.

Figure 5. Comparison of gas separation performance between x-SCPIM-y/z membrane and thermally cross-linked PIM-l membrane.

Table 1. Evaluation of the model performance on the reserved test set.

Gas	Training size	Training R	Test size	Test R
N2	514	0.986	172	0.847
O2	523	0.985	175	0.903
H2	324	0.985	109	0.827
He	282	0.980	94	0.799
CH4	420	0.990	141	0.904
CO2	471	0.986	158	0.875

Figure 6. Assisted design of high-performance polymer membranes.

Figure 7. Identification of polymer structures from a machine learning-aided design.

Figure 8. Polymer candidates for advanced CO₂/CH₄ gas transport properties and their experimental properties.

Data availability

The figures and tables used to support the findings of this study are included in the article.