CPC G16C 20/70 (2019.02) | 26 Claims |
1. A method, comprising:
creating a digital formulation graph comprising a plurality of nodes and a plurality of edges based on a formulation for a chemical product, wherein:
a root node represents the chemical product;
a remainder of the plurality of nodes each represent a respective ingredient of the formulation; and
each of the plurality of edges exists between a respective parent node and a respective child node;
providing a respective embedding vector for each respective node;
inputting the digital formulation graph and embedding vectors to a graph neural network (GNN) trained to produce a feature vector for the digital formulation graph based on the embedding vectors and an architecture of the GNN;
inputting the feature vector to a supervised machine learning model trained to predict an attribute value of the chemical product based on the feature vector; and
receiving a prediction of the attribute value of the chemical product from the supervised machine learning model.
|