用神经网络预测烷烃的物性
Predicting physical properties of alkanes with neural networks
摘要：
我们训练人工神经网络来预测直链、单支链和双支链烷烃的物理性质。这些神经网络可以从碎片数据中训练，这使我们能够使用物理属性信息作为输入，并利用属性之间的相关性来提高我们的预测质量。我们使用一组五个化学描述符来描述每一种烷烃的特征。我们建立了支化和沸点、热容和蒸气压之间的相关性，作为温度的函数。我们建立了对称性如何影响的熔点，并确定在直链烷烃的闪点错误的数据条目。最后，我们利用温度和压力的剪切粘度和密度的依赖性，以模拟线性烷烃的运动粘度。神经网络模型的准确性相比，有利的几个物理化学/热力学方法的准确性。
英文摘要：
We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods.
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