BPNN-QSTR Models for Triazine Derivatives for Lubricant Additives

Abstract

Abstract Triazine derivatives are a kind of lubricant additives with excellent tribological properties. It is of great significance to study the quantitative relationship between their chemical structure and tribological properties. In the present study, the quantitative structure tribo-ability relationships (QSTR) between 20 triazine derivatives and their respective extreme-pressure properties as lubricant additives were analyzed by the back propagation neural network (BPNN) method. The BPNN-QSTR model had satisfactory stability and predictive ability (R2 = 0.9965, R2(LOO) = 0.9195, q2 = 0.8274). The anti-wear model also yielded good predictions (R2 = 0.9757, R2(LOO) = 0.6261, q2 = 0.8022). Two- and three-dimensional structural descriptors were used to analyze molecular structures that affected extreme-pressure and anti-wear properties. The results indicate that the three-dimensional molecular dimensions and the bonding modes of the skeleton atoms in the molecules were important factors. In addition, the effects of N, P, O, and other hetero-atoms on the tribological properties were reflected in their corresponding group types and electronic structures.