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Graph Convolutional Neural Network Applied to the Prediction of Normal Boiling Point



Chen Qu, Anthony J. Kearsley, Barry I. Schneider, Walid Keyrouz, Thomas C. Allison


In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points. Data are drawn from the experimental literature as captured in the NIST Thermodynamics Research Center (TRC) SOURCE Data Archival System. The machine learning model is based on a graph neural network approach, a methodology that has proven powerful when applied to a variety of chemical problems. Model input is extracted from a 2D sketch of the molecule, making the methodology suitable for rapid prediction of normal boiling points in a wide variety of scenarios. Our final model predicts normal boiling points within 6 K (corresponding to a mean absolute percent error of 1.32 %) with sample standard deviation less than 8 K. Additionally, we found that our model robustly identify errors in the input data set during the model training phase, thereby further motivating the utility of systematic data exploration approaches for data-related efforts
Journal of Molecular Graphics and Modelling


Boiling Point, Machine Learning, Graph Neural Network


qu, C. , Kearsley, A. , Schneider, B. , Keyrouz, W. and Allison, T. (2022), Graph Convolutional Neural Network Applied to the Prediction of Normal Boiling Point, Journal of Molecular Graphics and Modelling, [online], (Accessed April 19, 2024)
Created February 4, 2022, Updated March 28, 2024