Transformer-based graph neural network trained with structural information encoding

A computerized method, comprising:

• during a training phase,
• providing a training data set including a plurality of training data pairs, each of the training data pairs including a pre-transformation molecular graph and post-transformation energy parameter value representing an energy change in a molecular system following an energy transformation, wherein the pre-transformation graph includes a plurality of normal nodes connected by edges, each normal node representing an atom in the molecular system;
• encoding structural information in each pre-transformation molecular graph as learnable embeddings, the structural information describing the relative positions of the atoms represented by the normal nodes, the structural information including:
• an edge encoding representing a type of bond between a pair of the normal nodes in each pre-transformation molecular graph;
• and
• a spatial encoding representing a shortest path distance along the edges between the pair of the normal nodes in each pre-transformation molecular graph;
• and
• inputting the training data set to a transformer-based graph neural network to thereby train the transformer-based graph neural network to perform an inference at inference time.

A computing system, comprising:

• a processor configured to:
• during a training phase,
• provide a training data set including a plurality of training data pairs, each of the training data pairs including a pre-transformation graph and post-transformation parameter value representing a change in a system modeled by the pre-transformation graph following a transformation, wherein the pre-transformation graph includes a plurality of normal nodes connected by edges, each normal node representing a location in the system;
• encode structural information in each pre-transformation graph as learnable embeddings, the structural information describing the relative positions of the locations represented by the normal nodes, the structural information including:
• an edge encoding representing a type of connection between a pair of the normal nodes in each pre-transformation graph;
• and
• a spatial encoding representing a shortest path distance along the edges between the pair of the normal nodes in each pre-transformation graph;
• and
• input the training data set to a transformer-based graph neural network to thereby train the transformer-based graph neural network to perform an inference at inference time.