The present application relates to a method for quantitative analysis of a polymer structure. Specifically, the method may be carried out through steps of measuring rheological properties and/or molecular weight distribution of the arbitrarily selected polymer, setting a random value for the selected polymer and then predicting the rheological property and/or the molecular weight distribution of the polymer from the random value, and comparing the measured value with the predicted value to determine the value of the structural parameter of the polymer.

The present application relates to a method for quantitative analysis of a polymer structure. Specifically, the method may be carried out through steps of measuring rheological properties and/or molecular weight distribution of the arbitrarily selected polymer, setting a random value for the selected polymer and then predicting the rheological property and/or the molecular weight distribution of the polymer from the random value, and comparing the measured value with the predicted value to determine the value of the structural parameter of the polymer.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Disclosed is a system and method for natural language translation and reconciliation of colloquial chemical terminology.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.