Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Carbon materials possess active sites and functionalities on the surface that can attract prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to predict the optimized methane uptake, adsorption energy (Ead), and adsorbent rediscovery through multitechniques of neural, regression, classifier ML-DFT, and Uniform Manifold Approximation and Projection (UMAP). Nitrogen and oxygen (N/O) functionalities and graphene, graphene oxide (GO), and N-doped GO were applied to the methane storage medium. Multi-ML algorithms were employed for the adsorption energy of CH4 uptake on (i) N/O functionalities such as pyridinic (N-py), carboxyl (O–II), oxidized (N-x), hydroxyl (O-h), Nitroso (N-ni), and Amine (primary, secondary, and tertiary). (ii) The graphene surfaces are decorated with N/O heteroatoms to construct graphene oxide (GO) and N-doped GO. The DFT calculations were applied by PW91 and the Dmol3 package. N/O-functionalities in the distance of ∼2.0 to 3.1 Å groups obtained Ead of approximately −2.0 to −4 eV. Further, ML models accomplished the forthcoming rediscovery of CH4 physisorption by using the multiadsorptive features of optimized adsorbents with an R2 of 0.99. ML-derived sensitivity analysis approach was applied to specifications such as deformation adsorption energy, N/O functionality type, and optimized structure. CH4 adsorption specifications indicate sensitivity levels of −0.03 to 0.02 eV. The synergetic DFT/ML approaches distinguished the modeled and rediscovered phases of CH4 adsorption on N/O functional groups and graphene structures. UMAP is employed as a new adsorbent screening approach to play a complementary role in the ML modeling process.