An image-based machine learning (ML) method is introduced to predict the surface tension of an ethanol-water pendant drop made of unknown ethanol-water composition. In contrast to previous neural-network based surface tension predictors that rely on using either the simplified molecular-input line-entry system or experimentally measured surface tension values, the present image-based deep neural network model directly predicts surface tensions based on arbitrary pendant drop shapes at any stage before breakup. Using convolutional neural networks (CNNs), surface tension values are accurately obtained independent of the drop size and liquid properties. Two CNN architectures are presented that accurately predict the surface tension of a pendant drop for three different ML models. To improve the generality of the ML models, image data augmentation technique is used to generate more representatives from available data. Approximating the surface tension for unknown pendant drops outside the range of the given classes has also been demonstrated. The trained machine learning models have an overall accuracy of about 98% in predicting the surface tension of a pendant drop containing an unknown ethanol-water composition. Additionally, the ML models are tested on unknown methanol-water mixtures to demonstrate the generality and the results show good predicting accuracy. Besides the accuracy of CNN models, performance measures including Precision, Recall and F1-score are reported for each surface tension value in the dataset. Compared with the physics-based axisymmetric drop shape analysis techniques, the present method is not limited to equilibrium pendant drop images and is much faster and more versatile. This image-based ML method shows great promise in directly predicting the surface tension values based on vastly available pendant drop images.

引入了基于图像的机器学习（ML）方法来预测由未知的乙醇-水组成制成的乙醇-水悬滴的表面张力。与以前的基于神经网络的表面张力预测器依赖于使用简化的分子输入线输入系统或通过实验测量的表面张力值相比，本基于图像的深层神经网络模型直接基于任意悬垂下降来预测表面张力分手前任何阶段的形状。使用卷积神经网络（CNN），可以精确获得表面张力值，而与液滴大小和液体性质无关。提出了两种CNN体系结构，可针对三种不同的ML模型准确预测悬垂液滴的表面张力。为了提高ML模型的通用性，图像数据增强技术用于从可用数据中生成更多代表。还证明了在给定类别范围之外的未知悬垂液滴的近似表面张力。训练有素的机器学习模型在预测包含未知乙醇-水成分的悬垂液滴的表面张力时，总体精度约为98％。此外，在未知的甲醇-水混合物上测试了ML模型，以证明其通用性，并且结果显示出良好的预测准确性。除了CNN模型的准确性外，性能指标还包括 训练有素的机器学习模型在预测包含未知乙醇-水成分的悬垂液滴的表面张力时，总体精度约为98％。此外，在未知的甲醇-水混合物上测试了ML模型，以证明其通用性，并且结果显示出良好的预测准确性。除了CNN模型的准确性外，性能指标还包括 训练有素的机器学习模型在预测包含未知乙醇-水成分的悬垂液滴的表面张力时，总体精度约为98％。此外，在未知的甲醇-水混合物上测试了ML模型，以证明其通用性，并且结果显示出良好的预测准确性。除了CNN模型的准确性外，性能指标还包括报告数据集中每个表面张力值的精度，召回率和F1得分。与基于物理的轴对称液滴形状分析技术相比，本发明方法不仅限于平衡悬垂液滴图像，而且速度更快且用途更多。这种基于图像的ML方法在基于大量可用的悬垂液滴图像直接预测表面张力值方面显示出巨大的希望。