  Flash points of organic molecules play an important role in preventing
flammability hazards and large databases of measured values exist, although
millions of compounds remain unmeasured. To rapidly extend existing data to new
compounds many researchers have used quantitative structure-property
relationship (QSPR) analysis to effectively predict flash points. In recent
years graph-based deep learning (GBDL) has emerged as a powerful alternative
method to traditional QSPR. In this paper, GBDL models were implemented in
predicting flash point for the first time. We assessed the performance of two
GBDL models, message-passing neural network (MPNN) and graph convolutional
neural network (GCNN), by comparing methods. Our result shows that MPNN both
outperforms GCNN and yields slightly worse but comparable performance with
previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of
MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable
studies. To further explore GBDL models, we collected the largest flash point
dataset to date, which contains 10575 unique molecules. The optimized MPNN
gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We
also extracted 5 datasets from our integrated dataset based on molecular types
(acids, organometallics, organogermaniums, organosilicons, and organotins) and
explore the quality of the model in these classes.against 12 previous QSPR
studies using more traditional
