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Friction

Keywords

quantitative structure tribo-ability relationship, Bayesian regularization neural network, lubricant additive, antiwear

Abstract

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.

Publisher

Tsinghua University Press

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