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Using Graph Nets (GNs) to predict molecular properties


In this talk we will be showing how Graph Nets (GNs)—a set of statistical models that directly operate on molecular topology by updating and aggregating information between atoms and bonds—can approximate per-atom, per-bond, and per-molecule properties derived by quantum mechanics (QM), with errors within the uncertainty thereof, and an over-500-fold speed up


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