Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction. (arXiv:2209.07300v1 [cond-mat.mtrl-sci])

Graph neural networks (GNNs) have drawn more and more attention from material
scientists and demonstrated a high capacity to establish connections between
the structure 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, we develop a
multi-task (MT) architecture based on DimeNet++ and mixture density networks to
improve the performance of such task. Taking CO adsorption on Cu-based
single-atom …

arxiv co2 graph graph neural networks networks neural networks