Abstract: A formulation graph convolution network (F-GCN) with multiple GCNs assembled in parallel and connected to filters and an external learning architecture is able to predict the effectiveness of a formulation. Input into the multiple GCNs are molecular structures of formulants, which are processed as molecular graphs and output as molecular descriptors. The molecular descriptors are filtered by normalized ratios or fractions of the ingredient molecules in a formulation, such as a battery electrolyte or solvent. A formulation descriptor combines the filtered molecular descriptors to arrive at a predicted performance for the formulation, such as the battery capacity for an electrolyte formulation, by an external learning architecture. F-GCN may use a pre-trained GCN with physico-chemical properties of known molecular structures.

Abstract: Techniques regarding the synthesis of one or more polymers through one or more ring-opening polymerizations conducted within a flow reactor and facilitated by one or more anionic catalysts are provided. For example, one or more embodiments can comprise a method, which can comprise polymerizing, via a ring-opening polymerization within a flow reactor, a cyclic monomer in the presence of one or more anionic organocatalysts.

Abstract: Techniques regarding the synthesis of one or more polymers through one or more ring-opening polymerizations conducted within a flow reactor and facilitated by one or more anionic catalysts are provided. For example, one or more embodiments can comprise a method, which can comprise polymerizing, via a ring-opening polymerization within a flow reactor, a cyclic monomer in the presence of one or more anionic organocatalysts.

Abstract: A method, computer program product, and computer system are provided for featurization of carbon dioxide capture characteristics in molecules. The method inputs a structure-based key of a fixed number of sub-structure descriptors for chemical groups relating to carbon dioxide capture characteristics. Sub-structure searching of each of the sub-structures defined in the key is carried out through candidate molecules represented in a format suitable for searching. A featurization of each candidate molecule is provided in the form of a fixed bit fingerprint indicating a presence or an absence of the sub-structures defined in the key. The fingerprint is applied for screening of molecules for carbon dioxide capture characteristics.

Abstract: Techniques regarding the synthesis of one or more polymers of a target polymer class are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a recommendation component that can generate a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data.

Abstract: A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.

Abstract: A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorganisms. The rows of the adjacency matrix correspond to biosamples, and the columns correspond to instances of metadata and drugs associated with one or more of the biosamples. The elements of the adjacency matrix are assigned non-zero numerical values or zero depending on whether a known association exists. The adjacency matrix is then factored using a selected number of latent factors, thereby producing a reconstruction matrix approximating the adjacency matrix. The values of the reconstruction matrix are used to predict antimicrobial susceptibility of a biosample ID to a drug when antibiogram data are lacking.

Abstract: Provided is a compound that can be used as a base resin for a photosensitive resin composition. The photosensitive resin can form a fine pattern and can achieve high resolution without impairing mechanical strength and solubility. The compound is represented by the general formula (1): wherein Z represents a linear, branched or cyclic divalent hydrocarbon group having 2 to 30 carbon atoms; X1 to X3 represent any of —CO2—, —CONRX1—, —O—, —NRX1—, —S—, —SO2—, —SO3— and —SO2NRX1— and may be the same as or different from each other, provided that RX1 is a hydrogen atom or a monovalent hydrocarbon group having 1 to 30 carbon atoms; Ar represents a divalent aromatic group having 2 to 30 carbon atoms; L1 and L2 independently represent a divalent hydrocarbon group having 1 to 30 carbon atoms; and x and y are each independently 0 or 1.

Abstract: Techniques regarding post polymerization modifications to polycarbonate polymers via a flow reactor are provided. For example, one or more embodiments described herein can comprise a cyclic carbonate monomer that can be employed to facilitate polymerization of one or more polycarbonate platforms susceptible to post polymerization modification. For instance, one or more embodiments can regard a cyclic carbonate molecular backbone covalently bonded to an aryl halide functional group via in accordance with a chemical structure selected from the group consisting of: In the chemical structures, “R1” can be selected from the group consisting of a hydrogen atom and a functional group comprising a first alkyl group; “L” can represent a linkage group, comprising: a second alkyl group and an end group having at least one member selected from the group consisting of an oxygen atom and a nitrogen atom; and “A” can represent the aryl halide functional group.

Abstract: A method for synthesizing cyclic carbonate monomers using carbon dioxide (CO2) is provided. The method also includes combining reagents to synthesize the cyclic carbonate monomer, the reagents including a substrate that is a 1,X-diol, where X is between 2 and 5, a base that is a tertiary amine, a promoter that is a multidentate, bis-tertiary amine base where nitrogens are separated by 2 to 4 carbon atoms, a solvent, and CO2.

Abstract: Techniques regarding autonomously controlling one or more chemical reactors using generative machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a model component that can build a generative machine learning model based on training data regarding a past chemical reactor operation. The generative machine learning model can generate a recommended chemical reactor control setting for experimental discovery of a polymer.

Abstract: Techniques regarding post polymerization modifications to polycarbonate polymers via a flow reactor are provided. For example, one or more embodiments described herein can comprise a cyclic carbonate monomer that can be employed to facilitate polymerization of one or more polycarbonate platforms susceptible to post polymerization modification. For instance, one or more embodiments can regard a cyclic carbonate molecular backbone covalently bonded to an aryl halide functional group via in accordance with a chemical structure selected from the group consisting of: In the chemical structures, “R1” can be selected from the group consisting of a hydrogen atom and a functional group comprising a first alkyl group; “L” can represent a linkage group, comprising: a second alkyl group and an end group having at least one member selected from the group consisting of an oxygen atom and a nitrogen atom; and “A” can represent the aryl halide functional group.

Abstract: A computer implemented method generating query results is provided. The method includes generating by a computer processor, a training model through artificial intelligence. The training model may be based on annotated data. A knowledge base for a subject matter may be generated based on the training model. The knowledge base may be based on content from document sources related to the subject matter. A natural language query input from a user may be received. An intent and requirements for satisfying the intent may be inferred by the computer processor. The knowledge base may be referenced to extract information related to the intent and requirements, from documents in the knowledge base. Relationships between the extracted information and the requirements may be correlated from the documents in the knowledge base. In addition, query results may be displayed to the user. The query results are based on the correlated relationships.

Abstract: A computational system, which may be an artificial intelligence (AI) system, allows for the electronic representation, modeling, generation, rendering, simulating, and querying of polymeric materials by encoding architectural features as nodes and embedding a directed graph of the polymeric materials within a database system. The embedding of chemical data from multiple nodes within the directed graph data structure enables a scalable data model for containing materials data within computational systems. The computational system may be used for predictive modeling of new polymeric materials and for rendering and/or simulating stochastic polymeric ensembles.

Abstract: Commands and constraints of reconfigurable instrumentation that execute unit operations of processes can be defined. A simulation engine configured to execute commands of the reconfigurable instrumentation can be defined such that processes can be simulated based on pre-defined mathematical models. A process historian configured to monitor and record results of the simulation can be defined. A first process can be simulated based on a first process configuration. The first process configuration can be mapped onto real-world reconfigurable instrumentation within the first process configuration.

Abstract: A processor may receive molecular data for a plurality of molecules. The processor may perform topological data analysis on the molecular data to generate a molecular topological map. The processor may identify one or more lacunae in the molecular topological map. The processor may generate one or more additional molecules to fill at least one of the one or more lacunae.

Abstract: Provided is a polymer that can be used as a base resin for a positive photosensitive resin composition and a negative photosensitive resin composition, wherein the positive photosensitive resin composition and the negative photosensitive resin composition are soluble in an aqueous alkaline solution, can form a fine pattern, can achieve high resolution, and have good mechanical properties even when they are cured at low temperature. Also provided are a positive photosensitive resin composition and a negative photosensitive resin composition using the polymer. The polymer is represented by general formulas (1) and/or (2): wherein T1 and T2 may be the same as, or different from, each other and represent any of —CO— and —SO2—; X1 is a tetravalent organic group; and l is 0 or 1; and X2 is a divalent organic group.

Abstract: Techniques regarding the synthesis of polyesters and/or polycarbonates through one or more ring-opening polymerizations conducted within a flow reactor and facilitated by a urea anion catalyst and/or a thiourea catalyst are provided. For example, one or more embodiments can comprise a method, which can comprise polymerizing, via a ring-opening polymerization within a flow reactor, a cyclic monomer in the presence an organocatalyst comprising a urea anion.

Abstract: A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.

Abstract: Techniques regarding the synthesis of one or more polymers through one or more ring-opening polymerizations conducted within a flow reactor and facilitated by one or more anionic catalysts are provided. For example, one or more embodiments can comprise a method, which can comprise polymerizing, via a ring-opening polymerization within a flow reactor, a cyclic monomer in the presence of one or more anionic organocatalysts.