Abstract: Information extraction from domain-specific documents can include performing named entity recognition thereon. A regular expression tagger can tag regular expressions in the domain-specific documents without pre-annotated training sets for the regular expression tagger. Defined domain-specific terms can be augmented with terms and phrases related to the defined domain-specific terms based on rules specified for desired information about named entities to be extracted. A dictionary-based tagger can tag the defined and augmented domain-specific terms and phrases in the plurality of domain-specific documents. Conflicts of named entity recognition between the regular expression tagger and the dictionary-based tagger can be resolved. The desired information about named entities can be extracted. Classification can be performed to identify whether the domain-specific documents are related to the desired information.

Abstract: Chemical formulations for chemical products can be represented by digital formulation graphs for use in machine learning models. The digital formulation graphs can be input to graph-based algorithms such as graph neural networks to produce a feature vector, which is a denser description of the chemical product than the digital formulation graph. The feature vector can be input to a supervised machine learning model to predict one or more attribute values of the chemical product that would be produced by the formulation without actually having to go through the production process. The feature vector can be input to an unsupervised machine learning model trained to compare chemical products based on feature vectors of the chemical products. The unsupervised machine learning model can recommend a substitute chemical product based on the comparison.

Abstract: Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.

Abstract: Chemical formulations for chemical products can be represented by digital formulation graphs for use in machine learning models. The digital formulation graphs can be input to graph-based algorithms such as graph neural networks to produce a feature vector, which is a denser description of the chemical product than the digital formulation graph. The feature vector can be input to a supervised machine learning model to predict one or more attribute values of the chemical product that would be produced by the formulation without actually having to go through the production process. The feature vector can be input to an unsupervised machine learning model trained to compare chemical products based on feature vectors of the chemical products. The unsupervised machine learning model can recommend a substitute chemical product based on the comparison.

Abstract: Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.

Abstract: Chemical formulations for chemical products can be represented by digital formulation graphs for use in machine learning models. The digital formulation graphs can be input to graph-based algorithms such as graph neural networks to produce a feature vector, which is a denser description of the chemical product than the digital formulation graph. The feature vector can be input to a supervised machine learning model to predict one or more attribute values of the chemical product that would be produced by the formulation without actually having to go through the production process. The feature vector can be input to an unsupervised machine learning model trained to compare chemical products based on feature vectors of the chemical products. The unsupervised machine learning model can recommend a substitute chemical product based on the comparison.

Abstract: Machine vision technology can be used to predict a property of a product generated by a chemical process. The prediction can be based on an analytical characterization of the chemical process or the product generated by the chemical process with a detector that generates series data. The series data can be converted to an image and input to an artificial neural network (ANN) trained to predict the property of the product based on the image. A prediction of a property of the product can be received from the ANN and used to adjust the chemical process or to determine whether to reject the product.

Abstract: A reaction composition contains (a) an allyl polyether having the following formula: CH2=CHCH2O-Aa-B where, (i) subscript a is 2 to 170; (ii) A is selected from: —CH2CH2O—; —CH2CH(CH3)O—; —CH(CH3)CH2O—, CH2CH(CH2CH3)O—; —CH(CH2CH3)CH2O, —CH2CF(CF3)O—, —CF(CF3)CF2O— and —CF2CF(CF3)O—; and (iii) B is selected from —H, —CH3, —CH2CH3, —CH2CH2CH3, —CH2CH2CH2CH3, —C(O)CH3, and —CF2CF2CF3; (b) A silyl hydride functional siloxanc comprising the following siloxane units [R2HSiO1/2]m[R2SiO2/2]d[R2SiO3/2]t[SiO4/2]q wherein d+t+q is one or more and wherein: (i) R is selected from hydrocarbyl groups liaing from one to 8 carbon atoms; (ii) subscript m is 2 or more; (iii) subscript d is zero to 20; (iv) subscript t is zero to 2; (v) subscript q is zero to 2; and (c) a platinum-based hydrosilylation catalyst; where there are at least 4 molar equivalents of silyl hydride functionalities relative to allyl functionalities in the reaction composition.