Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning

Accurately evaluating the adsorption ability of adsorbents for heavy metal ions (HMIs) and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents. This paper presents an AI approach using deep neural networks and transfer learning to predict the adsorption ability of adsorbents at arbitrary sites. Using three HMIs (Pb(II), Hg(II), and Cd(II)) adsorbed on graphitic-C3N4 as a case study, the method achieves prediction accuracy comparable to ab initio DFT calculations but is millions of times faster. Experimental verification confirms the AI prediction's accuracy, highlighting the approach's efficiency and potential for broader applications in environmental remediation.

Zhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li