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 = 0.8...
