A Quantitative Structure Tribo-ability Relationship Model for the Antiwear Properties of N/S-containing Heterocyclic Lubricant Additives using Back Propagation Neural Network

Quantitative structure tribo-ability relationship (QSTR) was studied by back propagation neural network (BPNN) method for 31 kinds of N/S-containing heterocyclic lubricant additives. The BPNN-QSTR model exhibits good accuracy and predictability (R²=0.998 4, R²(LOO)=0.695 9, q²=0.879 1). Considering the sensitivities of 13 kinds of 2D and 3D structural descriptors included in BPNN inputs, the effects of each descriptor on antiwear performance were discussed. The results show that N and S heteroatoms had significant impacts on the antiwear performance. In addition, the antiwear performance was also affected on the length of molecule, the number of S with one double bond and aromatic rings, and branching degree of molecule.