quantitative structure tribo-ability relationship, Bayesian regularization neural network, lubricant additive, antiwear
Quantitative structure-activity relationship methods are used to study the quantitative structure tribo- ability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of structure descriptors. Here, we used the Bayesian regularization neural network (BRNN) to establish a QSTR prediction model. Two-dimensional (2D) BRNN–QSTR models can flexibly and easily estimate lubricant-additive antiwear properties. Our results show that electron transfer and heteroatoms (such as S, P, O, and N) in a lubricant-additive molecule improve the antiwear ability. We also found that molecular connectivity indices are good descriptors of 2D BRNN–QSTR models.
Tsinghua University Press
Xinlei GAO, Kang DAI, Zhan WANG et al. Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network. Friction 2016, 4(2): 105-115.