Graph Neural Networks for Prediction of Fuel Ignition Quality

Prediction
of combustion-related properties of (oxygenated) hydrocarbons
is an important and challenging task for which quantitative structure–property
relationship (QSPR) models are frequently employed. Recently, a machine
learning method, graph neural networks (GNNs), has shown promising
results for the prediction of structure–property relationships.
GNNs utilize a graph representation of molecules, where atoms correspond
to nodes and bonds to edges containing information about the molecular
structure. More specifically, GNNs learn physicochemical properties
as a function of the molecular graph in a supervised learning setup
using a backpropagation algorithm. This end-to-end learning approach
eliminates the need for selection of molecular descriptors or structural
groups, as it learns optimal fingerprints through graph convolutions
and maps the fingerprints to the physicochemical properties by deep
learning. We develop GNN models for predicting three fuel ignition
quality indicators, i.e., the derived cetane number (DCN), the research
octane number (RON), and the motor octane number (MON), of oxygenated
and nonoxygenated hydrocarbons. In light of limited experimental data
in the order of hundreds, we propose a combination of multitask learning,
transfer learning, and ensemble learning. The results show competitive
performance of the proposed GNN approach compared to state-of-the-art
QSPR models, making it a promising field for future research. The
prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.