Heterogeneous Molecular Graph Neural Network

Graph neural networks (GNN) have become very useful in predicting the quantum mechanical properties of molecules as they can model complex interactions. Most methods treat molecules as molecular graphs where atoms are represented as nodes and their chemical environment is characterized by their pairwise interactions with other atoms. However, few methods explicitly take many-body interactions into consideration, those between three or more atoms.

Introducing Heterogeneous Molecular Graphs (HMGNG)

To address the shortcomings of existing methods, a new graph representation of molecules called heterogeneous molecular graph (HMG) has been introduced. HMGs contain nodes and edges of various types, allowing for the modeling of many-body interactions. This enables HMGs to carry complex geometric information that was not possible with previous methods.

Heterogeneous Molecular Graph Neural Networks (HMGNN)

With the new HMGs, a new neural network called heterogeneous molecular graph neural network (HMGNN) has been built using a neural message passing scheme. The HMGNN model also incorporates global molecule representations and an attention mechanism to improve the prediction process. The HMGNN predictions are invariant to translation and rotation of atom coordinates and permutation of atom indices.

State-of-the-art Performance on QM9 dataset

The HMGNN model has outperformed other models in nine out of twelve prediction tasks on the QM9 dataset. The QM9 dataset contains information on 133k small organic molecules, including their 13 properties such as energy, heat capacity, etc.

In summary, the HMGNN is a new graph neural network model built on a novel graph representation of molecules called heterogeneous molecular graphs. This representation allows for the modeling of many-body interactions and carries complex geometric information. The incorporation of global molecule representations and attention mechanisms improved the prediction process. HMGNN has achieved state-of-the-art performance on the QM9 dataset, outperforming other models.

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