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

Abstract

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. However, predicting adsorption capabilities of adsorbents at arbitrary sites is challenging, with currently unavailable measuring technology for active sites and the corresponding activities. Here, we present an efficient artificial intelligence (AI) approach to predict the adsorption ability of adsorbents at arbitrary sites, as a case study of three HMIs (Pb(II), Hg(II), and Cd(II)) adsorbed on the surface of a representative two-dimensional graphitic-C3N4. We apply the deep neural network and transfer learning to predict the adsorption capabilities of three HMIs at arbitrary sites, with the predicted results of Cd(II) > Hg(II) > Pb(II) and the root-mean-squared errors less than 0.1 eV. The proposed AI method has the same prediction accuracy as the ab initio DFT calculation, but is millions of times faster than the DFT to predict adsorption abilities at arbitrary sites and only requires one-tenth of datasets compared to training from scratch. We further verify the adsorption capacity of g-C3N4 towards HMIs experimentally and obtain results consistent with the AI prediction. It indicates that the presented approach is capable of evaluating the adsorption ability of adsorbents efficiently, and can be further extended to other interdisciplines and industries for the adsorption of harmful elements in aqueous solution.

An end‐to‐end artificial intelligence platform enables real‐time assessment of superionic conductors

“…Then, by fine‐tuning the parameters of the “target model”, a model with similar accuracy as the “source model” is potentially obtained. This reduces the cost of data acquisition for the “target model.” TL has been used in NNs (CGCNN‐TL) for band gap ( E g ) prediction from low‐level Perdew–Burke–Ernzerhof (PBE) to high‐level Heyd–Scuseria–Ernzerhof (HSE06) functionals, 34 and applied in adsorption energy prediction between different adsorbents and heavy metal ions 35 . These works inspire us to apply TL to predict E a from calculation to experimental results in the future.…”

“…TL has been used in NNs (CGCNN-TL) for band gap (E g ) prediction from low-level Perdew-Burke-Ernzerhof (PBE) to high-level Heyd-Scuseria-Ernzerhof (HSE06) functionals, 34 and applied in adsorption energy prediction between different adsorbents and heavy metal ions. 35 These works inspire us to apply TL to predict E a from calculation to experimental results in the future. This process takes the initial model as the starting point and then fine-tunes it to obtain the final model.…”

Application of neural network in metal adsorption using biomaterials (BMs): a review

“…The optimization pathways reported in the literature are a compilation of variables influencing the design of adsorption systems and the adsorption process. 90 The variables affecting the adsorbent modification or preparation conditions and the metal adsorption efficacy include biomaterial dose, surface type and thermal treatments. Under the batch adsorption systems, the adsorption attributes that affect the process behaviour include the initial concentration of metal pollutants, pH of the aqueous solution, volume and medium of adsorbate solution, agitation or shaker speed, temperature and contact time.…”

Deep Learning Accelerates the Discovery of Two‐Dimensional Catalysts for Hydrogen Evolution Reaction

“…Regression methods in ML requires a lot of data, but the use of CGCNN classification model can quickly help us build a model with high accuracy from a small amount of data, which saves the cost of time. [32][33][34] Compared with the traditional NN model, the application of the proposed CGCNN model is not limited to 2D materials, but can also be applied to other materials, such as heterojunction, Cu-Al alloy, and even high-entropy alloy. [35,36] 2.…”