TY  - JOUR
AU  - Schweidtmann, Artur M.
AU  - Rittig, Jan G.
AU  - König, Andrea
AU  - Grohe, Martin
AU  - Mitsos, Alexander
AU  - Dahmen, Manuel
TI  - Graph Neural Networks for Prediction of Fuel Ignition Quality
JO  - Energy & fuels
VL  - 34
IS  - 9
SN  - 1520-5029
CY  - Columbus, Ohio
PB  - American Chemical Society
M1  - FZJ-2020-05115
SP  - 11395 - 11407
PY  - 2020
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000574904900087
DO  - DOI:10.1021/acs.energyfuels.0c01533
UR  - https://juser.fz-juelich.de/record/888680
ER  -