Method and apparatus for determining synthesis recipe using neural network model and training method of neural network model

A training method of a neural network model performed by at least one processor for determining a synthesis recipe, the training method comprising:

• receiving a training query comprising at least one of a product, a reactant, a reagent and a reaction condition;
• generating a target vector corresponding to the product for the synthesis recipe and a prediction vector corresponding to at least one of the reactant, the reagent, and the reaction condition by inputting the training query into the neural network model;
• and
• training, based on the target vector and the prediction vector, the neural network model to determine the synthesis recipe corresponding to the training query or an embedding vector of the synthesis recipe corresponding to the training query.

A non-transitory computer-readable storage medium having instructions stored therein, which when executed by a processor cause the processor to execute a method comprising:

• receiving a query comprising at least one of a product, a reactant, a reagent, and a reaction condition;
• obtaining a query embedding vector corresponding to the query by inputting the query into a neural network model;
• extracting a candidate embedding vector from among a plurality of embedding vectors based on a similarity between the query embedding vector and the plurality of embedding vectors corresponding to reaction records stored in a database;
• and
• outputting a synthesis recipe corresponding to the query by retrieving a candidate reaction record corresponding to the candidate embedding vector in the database.

An apparatus comprising:

• a communication interface configured to receive a query comprising at least one of a product, a reactant, a reagent, and a reaction condition;
• a memory configured to store one or more instructions and a neural network model;
• and
• a processor operatively coupled to the memory,
• wherein the one or more instructions, when executed by the processor, cause the apparatus to:
• obtain a query embedding vector corresponding to the query by inputting the query to the neural network model,
• extract a candidate embedding vector from among a plurality of embedding vectors based on a similarity between the query embedding vector and the plurality of embedding vectors corresponding to reaction records stored in a database, and
• output a synthesis recipe corresponding to the query by retrieving a candidate reaction record corresponding to the candidate embedding vector in the database.