Directed Message-Passing Neural Networks for Gas Chromatography

In this paper, the directed message-passing neural network architecture is used to predict several quantities of interest in gas chromatography: retention times, Clarke-Glew 3-point thermodynamic parameters for simulation, and retention indices. The retention index model was trained with 48,803 training samples and reached 1.9–2.6% accuracy, whereas the thermodynamic parameters and retention time were trained by using 230 training data samples yielding 17% accuracy. Furthermore, the accuracy as a function of the number of training samples is investigated, showing the necessity of large, accurate datasets for training deep learning-based models. Lastly, several uses of such a model for the identification of compounds and the optimization of GC parameters are discussed.