Invention Publication
- Patent Title: TRANSFORMER-BASED GRAPH NEURAL NETWORK TRAINED WITH STRUCTURAL INFORMATION ENCODING
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Application No.: US17806075Application Date: 2022-06-08
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Publication No.: US20230402136A1Publication Date: 2023-12-14
- Inventor: Shuxin ZHENG , Yu SHI , Tie-Yan LIU
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Main IPC: G16C20/70
- IPC: G16C20/70 ; G16C60/00 ; G16C20/50 ; G06N3/04 ; G06N5/04

Abstract:
A computing system is provided, including a processor configured to, during a training phase, provide a training data set, including a pre-transformation molecular graph and post-transformation energy parameter value representing an energy change in a molecular system following an energy transformation. The pre-transformation graph includes a plurality of normal nodes connected by edges representing a distance and a bond between a pair of the normal nodes. The processor is further configured to encode 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 includes an edge encoding representing a type of bond between a pair of normal nodes in each pre-transformation molecular graph, and a spatial encoding representing a shortest path distance along the edges between a pair of normal nodes in each pre-transformation molecular graph.
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