LG-Transformer: learned-graph transformer framework enabling diverse physicochemical properties prediction toward...

Green fuels are essential for decarbonizing transportation sectors, requiring accurate prediction of different physicochemical properties to optimize engine performance and emissions. Although artificial intelligence-based models demonstrate significant potential to accelerate fuel design, most existing methods cannot utilize the internal and external information within and between fuel molecules with interpretability, limiting their generalizability for diverse properties prediction. To address these challenges, a deep learning framework, the learned graph feature fusion Transformer (LG-Transformer), is proposed. Unlike conventional graph neural networks (GNNs) that operate on atom-bond molecular graphs, LG-Transformer employs contrastive learning to construct an inter-molecular relationship graph guided by topological descriptors and property similarity, enabling property-aware feature propagation through Transformer layers for various property prediction. Supporting this effort, a comprehensive fuel property database is developed, containing 1850 diverse molecules across 26 chemical classes, each annotated with 17 key physicochemical properties relevant to engine performance. Here we show that LG-Transformer achieves superior predictive performance with an average R² of 0.900, significantly outperforming other GNN and deep learning baselines. Additionally, interpretability analyses via integrated gradients reveal underlying molecular structure-property relationships.