Kun Young Choi  Woo Hyun Yuk  Jeong Woo Han  Cham Kill Hong

10.5626/JOK.2024.51.5.406

dehydrogenation enthalpy graph isomorphism network molecular embedding

This paper conducts dehydrogenation enthalpy prediction that could play an important role in selecting optimal liquid organic hydrogen carriers. We employed graph convolutional networks, which produced molecular embeddings for the prediction. Specifically, we adopted Graph Isomorphism Network (GIN) known to be the most expressive graph-based representation learning model. Our proposed approach could build molecular embeddings. Our proposed approach outperformed conventional machine learning solutions and traditional representations based on chemical physics algorithms. In addition, the performance of the proposed model could be improved with small batch sizes and deeper GCN layers using skip connections.