Author:
Gao Xinlei,Dai Kang,Wang Zhan,Wang Tingting,He Junbo
Abstract
Abstract
Quantitative structure-activity relationship methods are used to study the quantitative structure triboability 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
Springer Science and Business Media LLC
Subject
Surfaces, Coatings and Films,Mechanical Engineering
Cited by
21 articles.
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