International Business Machines Corporation

Agency for Science, Technology and Research

Leland Stanford Junior University

Institute of Bioengineering and Nanotechnology

Agency for Science, Technology

Columbia University

Institute of Bioengineering and Nanotechnology, Biomedical Sciences Institute

Nathaniel H Park

James L Hedrick

Yi Yan Yang

Victoria A Piunova

Mareva B Fevre

Rudy J Wojtecki

Dmitry Zubarev

Pang Kern Jeremy Tan

Chuan Yang

Gavin O Jones

Theodore Gerard Van Kessel

Amos Cahan

Xin Ding

Zhen Chang Liang

Dylan J Boday

Jeannette M Garcia

Robert M Waymouth

Zhi Xiang Voo

Binhong Lin

Luis Miguel Campos

Dino Wu

Tim Erdmann

Cherylette Anne Alexander

Hariklia Deligianni

Daniel Gruhl

Chandrasekhar Narayan

Alfredo Alba

Linda Ha Kato

Steven R Welch

Chad Eric DeLuca

Pei-Yun S Hsueh

Petar Ristoski

Anna Lisa Gentile

Mu San Zhang

Ashlynn Lingzhi Lee

Pedro Luis Arrechea

Junho Kwon

Daniel Sanders

Willy Chin

Wei Cheng

James Lupton Hedrick

Shaoqiong Liu

Jye Yng Teo

Jiayu Leong

Yang Chuan

Eunice Leong

Eddy Wei Ping Tan

Yiran Zhen

Daniel Malinowski

Jeremy Tan

YiYan Yang

Sarathkrishna Swaminathan

Caleb Nathaniel Jadrich

Vince Evan Pane

Sarath Swaminathan

Gowri Nayar

A method for receiving a textual description of a first chemical structure; applying a first machine learning model to the textual description to generate a first data structure indicating components of the first chemical structure and bonds between the components of the first chemical structure; applying a second machine learning model to the first data structure to generate a first description of the first chemical structure using a domain specific language; and querying a database using the first description such that the database outputs a second description of a second chemical structure using the domain specific language stored in the database.

Creating domain-specific language representations of chemical structures

A process, composition, and apparatus for carbon dioxide (CO) conversion are disclosed. The COconversion comprises reacting COwith a cyclopropenimine (CPI) to activate the COand transferring the activated COto generate a product of the transferring. The transferring also generates a conjugate acid of the CPI. The process further comprises regenerating the CPI from the conjugate acid.

Cyclopropenimines for activation of carbon dioxide

A process, apparatus, and cyclopropenimine (CPI) material for generating metal carbonates are disclosed. Generating the metal carbonates comprises reacting carbon dioxide (CO) with CPI. Generating the metal carbonates also comprises forming a reaction mixture comprising water, metal cations, and a product of the reacting.

Cyclopropenimines for mineralization of carbon dioxide

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.

Material representation in computational systems

A process, apparatus, and material for generating polymers are disclosed. Generating the polymers comprises reacting carbon dioxide (CO) with a cyclopropenimine (CPI). Generating the polymers further comprises reacting monomers with a product of the reaction between the CPI and the CO.

Candidate material for polymerization can be received. One or more desired features in the candidate material can be identified. A machine learning model can be trained to generate a new material having one or more of the desired features. Permissively, the candidate material can be determined from running a machine learning classification model that ranks a plurality of material as candidates. Permissively, the generated new material can be input to the machine learning classification model, for the machine learning classification model to include in ranking the plurality of material as candidates.

Expert-in-the-loop AI for materials generation

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, 'R' 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.

Post polymerization modification in flow

Techniques regarding the transportation of molecular cargo across the BBB are provided. For example, one or more embodiments described herein can comprise a chemical compound to facilitate molecular encapsulation of the molecular cargo. The chemical compound can comprise a diblock copolymer having a molecular backbone comprising a polycarbonate structure and a polyethylene glycol structure. Also, the polycarbonate structure can be functionalized with boronic acid.

Biodegradable functional polycarbonate nanoparticle carries for delivering molecular cargo

A set of material candidates expected to yield materials with target properties can be generated. A subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates can be received. Based on the subject matter expert's input, a machine learning model can be trained to replicate the subject matter expert's decision.

Expert-in-the-loop AI for materials discovery

A method for synthesizing cyclic carbonate monomers using carbon dioxide (CO) 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 CO.

AI assisted cyclic carbonate monomer synthesis

Techniques regarding a chemical composition that can be utilized within one or more combination therapies to treat a microbial infection are provided. For example, one or more embodiments described herein can comprise a chemical composition that includes a first triblock polymer comprising a quaternary ammonium functionalized polycarbonate block and exhibiting anticancer activity via a lytic mechanism. The chemical composition can also include a second triblock polymer comprising a guanidinium functionalized polycarbonate block and exhibiting anticancer activity via a translocation mechanism.

Combination therapies that enhance antimicrobial/anticancer activities

Compositions and methods regarding antimicrobial guanidinium macromolecules with one or more targeting moieties for selectively targeting bacteria are provided. According to an embodiment, an antimicrobial macromolecule is provided that comprises a polymer backbone and one or more guanidinium moieties that extend from the polymer backbone. The antimicrobial macromolecule further comprises a targeting moiety that extends from the polymer backbone. The targeting moiety can comprise a substance favored for consumption by bacteria, such as a monosaccharide.

Antimicrobial guanidinium macromolecules with bacteria targeting moieties

A process, composition, and apparatus for carbon dioxide (CO) sequestration are disclosed. The process includes passing air containing the COthrough a cyclopropeneimine (CPI)-functionalized material. The composition includes a CPI-functionalized material that reacts with COwhen mixed with air containing the CO. The apparatus includes a component for providing a CPI-functionalized material and mixing the material with COgas.

Cyclopropeneimines for capture and transfer of carbon dioxide

Techniques regarding chemical compounds comprising perfluoroaryl groups that can facilitate post-synthesis functionalization are provided. For example, one or more embodiments described herein can comprise a chemical compound. The chemical compound can comprise a molecular backbone. The chemical compound can also comprise a pendent functional group bonded to the molecular backbone. The pendent functional group can comprise a perfluoroaryl group and a methylene group.

Chemical compounds with perfluoroaryl groups that can facilitate post-synthesis functionalization

A stimulus-responsive micellar carrier, methods that may be associated with making a stimulus-responsive micellar carrier, and methods that may be associated with using a stimulus-responsive micellar carrier are disclosed. The stimulus-responsive micellar carrier comprises a cargo molecule, and a linear block copolymer having a hydrophilic block connected to a hydrophobic block by a stimulus-responsive junction moiety. The micellar carrier can be supplied to a patient body for therapeutic purposes, such as the treatment of cancerous tissue. A method of preparing or obtaining a stimulus-responsive micellar carrier may include preparing a polyethylene glycol material having an acetal end group and then preparing a block copolymer by forming a reaction mixture including the polyethylene glycol material, a cyclic carbonate monomer, and a base.

Stimulus-responsive micellar carrier

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San Jose, CA, United States of America

Nathaniel H Park