Abstract：
:Modern industrial lubricants are often blended with an assortment of chemical additives to improve the performance of the base stock. Machine learning-based predictive models allow fast and veracious derivation of material properties and facilitate novel and innovative material designs. In this study, we outline the design and training process of a general feed-forward artificial neural network that accurately predicts the dynamic viscosity of oil-based lubricant formulations. The network hyperparameters are systematically optimized by Bayesian optimization, and strongly correlated/collinear features are trimmed from the model. By harnessing domain knowledge in the selection of features, the quantitative structure-property relationship model is built with a relatively simple feature set and is versatile in predicting the dynamic viscosity of lubricant oils with and without enhancement by viscosity modifiers (VMs). Moreover, partial dependency, local-interpretable model-agnostic explanations, and Shapley values consistently show that the eccentricity index, Crippen MR, and Petitjean number are important predictors of viscosity. All in all, the neural model is reasonably accurate in predicting the dynamic viscosity of lubricant solvents and VM-enhanced lubricants with an R2 of 0.980 and 0.963, respectively.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Loh GC,Lee HC,Tee XY,Chow PS,Zheng JWdoi
10.1021/acs.jcim.9b01068subject
Has Abstractpub_date
2020-03-23 00:00:00pages
1224-1234issue
3eissn
1549-9596issn
1549-960Xjournal_volume
60pub_type
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