Institute of High Performance Computing, 1 Fusionopolis Way, #16-16 Connexis 138632, Singapore.
Institute of Chemical and Engineering Sciences, 1 Pesek Road, Jurong Island 627833, Singapore.
J Chem Inf Model. 2020 Mar 23;60(3):1224-1234. doi: 10.1021/acs.jcim.9b01068. Epub 2020 Mar 2.
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 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 feature set and is 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 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 of and , respectively.
现代工业润滑剂通常混合有各种化学添加剂，以提高基础油的性能。基于机器学习的预测模型允许快速准确地推导材料性能，并促进新颖和创新的材料设计。在这项研究中，我们概述了一种前馈人工神经网络的设计和训练过程，该网络可以准确预测油基润滑剂配方的动态粘度。通过贝叶斯优化系统地优化网络超参数，并从模型中修剪强相关/共线性特征。通过在特征选择中利用领域知识，建立了定量结构-性质关系模型，该模型具有相对较少的特征集，并能够准确预测有和没有粘度改性剂 (VM) 增强的润滑油的动态粘度。此外，偏依赖、局部可解释的无模型解释和 Shapley 值表明，偏心指数、 Crippen MR 和 Petitjean 数是粘度的重要预测因子。总之，该神经网络模型在预测润滑剂溶剂和 VM 增强润滑剂的动态粘度方面具有相当高的准确性，其分别为 和 。
