Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets

Machine learning (ML) approaches like neural networks have been widely used in chemical researches for fast estimating chemical properties. Generating ML models of good precision requires datasets of high quality, which can be difficult to obtain. In this work, we trained graph neural network (GNN) models from different datasets and verified transferring of the models to other datasets. Our result shows that cross-dataset evaluation can give less accurate but still correlative prediction results on different datasets. Errors are mainly due to systematic errors. The value range of prediction result is highly related to the range of training set. The precisions of different bonds show different distributions. C–H bond constantly gets the highest precision in the tested bonds.