Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning

The adsorption behavior of graphene oxide and metal ions was simulated theoretically by density functional theory. In the process of training the prediction model based on the machine learning method, the missing values were processed by matrix completion method, which was widely used in the recommendation systems, and gradient boosting machine (GBM) was trained to explain the importance of factors that affect the adsorption energy. The result showed that nine properties of the adsorption, namely ionic radius, zero-point vibration energy, Mulliken charge, boiling point, dipole moment, atomic weight, molar heat capacity at constant volume (CV), spin multiplicity and bond length, were found to provide 90% importance of the cumulative adsorption energy. Then six regression methods, including support vector regression, ridge regression, random forest, extremely randomized trees, extreme gradient boosting, and light gradient boosting machine, were used to quantitatively evaluate the prediction accuracy. The results showed that machine learning could provide sufficient accuracy to predict adsorption energy. Among them, extremely randomized trees displayed the best prediction performance, with a mean square error only 0.075. Furthermore, the trained model was tested in a system of vanillin adsorbing metal ions, verifying the feasibility of training the prediction model of adsorption energy based on machine learning But it is still necessary to be further improved. In general, this research takes the advantage of machine learning on the basis of saving experimental time to provide an instructive reference for theoretical research on metal ion removal.