Multi-Task Mixture Density Graph Neural Networks for Predicting Catalyst Performance

Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a strong capacity to establish connections between structures and properties. However, with only unrelaxed structures provided as input, few GNN models can predict the thermodynamic properties of relaxed configurations with an acceptable level of error. In this work, a multi-task (MT) architecture based on DimeNet++ and mixture density networks is developed to improve the performance of such task. Taking CO adsorption on Cu-based single-atom alloy catalysts as an example, the method can reliably predict CO adsorption energy with a mean absolute error of 0.087 eV from the initial CO adsorption structures without costly first-principles calculations. Compared to other state-of-the-art GNN methods, the model exhibits improved generalization ability when predicting the catalytic performance of out-of-distribution configurations, built with either unseen substrate surfaces or doping species. Further, the enhancement of expressivity has also been demonstrated on the IS2RE predicting task in the Open Catalyst 2020 project. The proposed MT GNN strategy can facilitate the catalyst discovery and optimization process.