Using machine learning for generating chemical product formulations

A method of synthesizing a chemical product comprising:

• preprocessing historic experiments data that includes ingredients used for synthesizing the chemical product in prior experiments, quantities of the ingredients used for the synthesis of the chemical product in the historic experiments, specifications of the chemical product that was synthesized and outcomes of the historic experiments;
• receiving information regarding independent features and dependent features from the historic experiments data;
• obtaining feature importance scores of the independent features and the dependent features using a trained machine learning (ML) model;
• constructing data structures encoding the historic experiments data based on the feature importance scores;
• extracting analytical rules from the data structures wherein the analytical rules include the ingredients and the quantities of ingredients to be used expressed in terms of mathematical operators and thresholds including minimum and maximum quantities of the ingredients;
• generating seed formulae from the analytical rules, the seed formulae including the ingredients and the quantities of ingredients to be used for the synthesis of the chemical product;
• and
• enabling a user to generate one or more intermediate formulae from the seed formulae via displaying the seed formula on a formulation graphical user interface (GUI) that enables the user to select specific quantities for each of the ingredients;
• and
• providing final formulae that correspond to validated intermediate formulae to a production system.

A non-transitory processor-readable storage medium comprising machine-readable instructions that cause a processor to:

• access historical experiments data that includes data regarding experiments for synthesis of a chemical product, the historical experiments data including ingredients used for making the chemical product, proportions of the ingredients required to make the chemical product and specifications associated with each of the experiments;
• extract features from the historical experiments data using a feature selection algorithm;
• calculate a feature importance score of each of the features using a machine learning (ML) algorithm;
• select a subset of the features based on the feature importance scores;
• build an ML model that includes a classification tree that encodes data pertaining to the subset of the features;
• extract rules for synthesizing the chemical product from the ML model;
• generate seed formulae for the synthesis of the chemical product from the rules, wherein the seed formulae include at least ingredients and quantities of the ingredients to be used for the synthesis of the chemical product;
• and
• provide one or more of the seed formulae that are validated to a production system for production of the chemical product.