Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field’s evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010–2026 and retained 730 valid records from 1581 initial publications after screening. VOSviewer, CiteSpace, and R were used to analyze publication growth, collaboration networks, journal sources, and thematic evolution. Results show that annual output remained generally below 20 before 2019, then increased rapidly and reached approximately 280 publications in 2025, indicating accelerated integration of machine learning with adsorption simulation, material screening, and performance evaluation. The source distribution broadened from a limited set of chemistry and engineering journals to diverse venues, with recent high publication weights in Chemical Engineering Journal, Separation and Purification Technology, ACS Applied Materials & Interfaces, Microporous and Mesoporous Materials, and Journal of Materials Chemistry A. Collaboration analysis identified 10 compact author clusters, including groups associated with Randall Q. Snurr, Seda Keskin, Zhiwei Qiao, Qingyuan Yang, and Chongli Zhong, whereas the weak bridging links among clusters indicate that cross-community collaboration remains limited. Country and institutional analyses show that China, the United States, Canada, Iran, India, South Korea, and the United Kingdom are leading contributors, with Guangzhou University, Koç University, Northwestern University, the Chinese Academy of Sciences, Beijing University of Chemical Technology, and the United States Department of Energy occupying prominent positions. Keyword evolution reveals a shift from adsorption behavior and porous adsorbents toward data-guided material selection, high-throughput screening, deep learning, Bayesian optimization, and performance optimization, offering guidance for data-driven adsorbent discovery.

We investigate dark photon emission from the final state leptons in the rare decay $D^0\to e^{+}{e}^{-}{A}^{\prime }$ within a point-like description of the parent transition. The physical electron mass and exact three-body kinematics are retained throughout the complete phase space for each dark photon mass in the interval $1\mathrm{MeV}\le m_{A^{\prime }}\le 1.80\mathrm{GeV}$. By normalizing the radiative width to the corresponding non-radiative width, we obtain dimensionless radiation factors that are independent of the unknown normalization of the effective $D^0\to e^{+}{e}^{-}$ amplitude. This formulation allows the mass dependence, polarization content, and spectral shape to be studied without fixing the poorly constrained kinetic mixing parameter. A covariant decomposition separates the transverse and longitudinal dark photon contributions, while normalized dilepton invariant mass spectra describe the event kinematics. Across the scanned mass interval, the mixing-independent pseudoscalar radiation factor decreases by more than nine orders of magnitude and approaches zero at the kinematic endpoint. The transverse modes dominate throughout the scanned interval, while the longitudinal fraction remains approximately $11\%$ to $20\%$. The normalized dilepton invariant mass spectrum shifts toward lower masses as $m_{A^{\prime }}$ increases. After normalization, the scalar and pseudoscalar radiation factors differ only through finite electron mass effects. These results provide a quantitative polarization-resolved benchmark for the point-like lepton final-state radiation component and can serve as baseline input for more complete phenomenological studies of rare charm decays.