Relationship modeling on quantitative structure-inhibitive efficiency of imidazoline inhibitors by combining random forest and multiple linear regression

Focusing on 15 different undecyl imidazoline corrosion inhibitors, a new method of combining random forest (RF) and multiple linear regression (MLR) was proposed to investigate the quantitative structure-inhibitive efficiency (IE) relationship. First, 15 corrosion inhibitors were comprehensively characterized by six aspects, which include energy, charge, molecular surface and information content, spatial and topological features, and 55 molecular structural features were achieved. Then RF and MLR were respectively employed to optimize these 55 features, so 8 overlapped parameters were selected from the top ten. Only 3 from 8 optimal features were randomly selected to construct the MLR model between the relationship of structure-IE. The optimal combination of features were molecular total energy (Te), information content (Ic) and molecular refractive index (Mr). Based on this, the optimal model of quantitative structure-inhibitive efficiency (IE) relationship was obtained, the correlation coefficient (R²) is 0.843, the relational expression is IE=-5.517-0.010 1Te+15.601 7Ic+0.222Mr. A singular sample was removed after the investigation of samples, its relative error reached 18.9%. The remaining 14 samples were modeled, the performance of the model was obviously further improved with the R² of 0.911. The results indicate that Te, Ic and Mr all show the high positive correlation with IE. When the molecular structure is more stable, the symmetry is good, and the refractive index is high, then the IE value is higher. The model may be used as a theoretical reference for the design of new corrosion inhibitors.