High‐throughput computational screening of porous polymer networks for natural gas sweetening based on a neural network

Abstract The capture and storage of toxic industrial chemicals such as H 2 S using porous polymer networks (PPNs) has shown promising application because of their high porosity, high surface area, high stability, low‐cost and lightweight. In this work, 17,846 PPNs with the diamond‐like topology were computationally screened to identify the optimal adsorbents for the removal of H 2 S and CO 2 from humid natural gas based on the combination of molecular simulation and machine learning algorithms. The top‐performing PPNs such as hPAFs‐0201 with the highest adsorption performance scores (APS) were evaluated and identified based on their adsorption capacities and selectivity for H 2 S and CO 2 . The strong affinity between water molecules and the framework atoms in a few PPNs has a significant impact on the adsorption selectivity of acid gases. Based on decision tree analysis, we found two main design paths of the optimal PPNs for natural gas sweetening, which are the PPNs with LCD ≤ 4.648 Å, V f ≤ 0.035, and PLD ≤ 3.889 Å, and those with 4.648 Å ≤ LCD ≤ 5.959 Å, ρ ≤ 837 kg m −3 . In addition, we constructed different machine learning models, particularly artificial neural network, available to accurately predict the APS of PPNs. 2D projection map of geometrical properties of PPNs using the t‐distributed stochastic neighbor embedding (t‐SNE) method shows that the screened 390 samples exhibit the similar structures. Among the top‐23 PPNs with the highest APS, hPAFs‐0201 has enhanced natural gas sweetening performance due to its strong affinity between the N‐rich organic linkers and acid gases. hPAFs‐0752 shows the highest isosteric adsorption heat of H 2 S and CO 2 ( Q ° st = 49.84 kJ mol −1 ), resulting in its second‐highest APS as well as high hydrophilicity. Based on the combination of molecular simulation and machine learning, comprehensive insights into the high‐throughput screening of PPNs in this work will provide new ideas for the design of high‐performance PPNs for gas separation.