公开(公告)号：US20230170056A1

公开(公告)日：2023-06-01

申请号：US17967711

申请日：2022-10-17

Applicant: X Development LLC

Inventor： Julia Yang ,  Vahe Gharakhanyan ,  Tusharkumar Gadhiya ,  Alexander Holiday

IPC: G16C20/10 ,  G16C60/00 ,  C08J11/10

CPC classification number: G16C20/10 ,  G16C60/00 ,  C08J11/10

Abstract: Methods may include accessing a first data set that includes a plurality of first data elements. Each of the plurality of first data elements may characterize a depolymerization reaction. Each first data element may include an embedded representation of a structure of a reactant and a reaction-characteristic value that characterizes a reaction between the reactant and a polymer. The embedded representation may be identified as a set of coordinate values within an embedding space. The method may include constructing a predictive function to predict reaction-characteristic values from embedded representations. The method may also include evaluating a utility function that transforms a given point within the embedding space into a utility metric. The method may include identifying particular points as corresponding to high utility metrics. The method may also include outputting a result that identifies a reactant corresponding to the particular point or a reactant structure corresponding to the particular point.

公开(公告)号：US20230170059A1

公开(公告)日：2023-06-01

申请号：US17967685

申请日：2022-10-17

Applicant: X Development LLC

Inventor： Tusharkumar Gadhiya ,  Falak Shah ,  Nisarg Vyas ,  Julia Yang ,  Vahe Gharakhanyan ,  Alexander Holiday

IPC: G16C20/70 ,  G16C20/20 ,  G16C20/80

CPC classification number: G16C20/70 ,  G16C20/20 ,  G16C20/80

Abstract: Computer-implemented methods may include accessing a multi-dimensional embedding space that supports relating embeddings of molecules to predicted values of a given property of the molecules. The method may also include identifying one or more points of interest within the embedding space based on the predicted values. Each of the one or more points of interest may include a set of coordinate values within the multi-dimensional embedding space and may be associated with a corresponding predicted value of the given property. The method may further include generating, for each of the one or more points of interest, a structural representation of a molecule by transforming the set of coordinate values included in the point of interest using a decoder network. The method may include outputting a result that identifies, for each of the one or more points of interest, the structural representation of the molecule corresponding to the point of interest.

公开(公告)号：US20240266005A1

公开(公告)日：2024-08-08

申请号：US18435957

申请日：2024-02-07

Applicant: X Development LLC

Inventor： Alexander Holiday ,  Vahe Gharakhanyan ,  Falak Shah ,  Nisarg Vyas ,  Tusharkumar Gadhiya

IPC: G16C20/10 ,  G16C20/30 ,  G16C20/70 ,  G16C20/80

CPC classification number: G16C20/10 ,  G16C20/30 ,  G16C20/70 ,  G16C20/80

Abstract: A computational platform for generating solid catalysts for depolymerization reactions is described. The platform may include a first generative model to determine synthesizable crystal structures that could be used as solid catalysts for depolymerization. The first generative model may determine synthesizability and/or stability of solid catalysts. The first generative model may take in voxel representations of a crystal structure and then use a variational autoencoder to encode into latent space. The first generative model may also include a property learning component to determine synthesizable crystals in latent space. Candidate materials may then be identified in the latent space and then decoded into a blurred voxel representation. The blurred voxel representation may be transformed to a crystal structure. The platform may include a second generational model for identifying crystal surfaces and/or adsorption sites. Adsorption energies can be predicted and solid catalyst candidates for depolymerization can be identified.

公开(公告)号：US20230170053A1

公开(公告)日：2023-06-01

申请号：US17967704

申请日：2022-10-17

Applicant: X Development LLC

Inventor： Vahe Gharakhanyan ,  Julia Yang ,  Tusharkumar Gadhiya ,  Alexander Holiday

IPC: G16C10/00 ,  G16C20/10 ,  G16C20/40 ,  G16C20/70

CPC classification number: G16C10/00 ,  G16C20/10 ,  G16C20/40 ,  G16C20/70

Abstract: Computer-implemented methods may include identifying a polymer for decomposition. The method may further include accessing, for an ionic liquid, one or more properties corresponding to the polymer. One or more properties may characterize a reaction between the polymer and the ionic liquid. The method may also include accessing a value of the property using a quantum-mechanical or thermodynamical method. The method may include determining a bond string and position (BSP) representation of a molecule of the ionic liquid. The method may further include determining an embedded representation of the ionic liquid based on the BSP representation. In addition, the method may include generating a relationship between BSP representations of molecules and the one or more properties. The method may also include identifying an ionic liquid as a prospect for depolymerizing the specific polymer based on the relationship. The method may include outputting an identification of the ionic liquid.

公开(公告)号：US20230167264A1

公开(公告)日：2023-06-01

申请号：US17967723

申请日：2022-10-17

Applicant: X Development LLC

Inventor： Tusharkumar Gadhiya ,  Falak Shah ,  Nisarg Vyas ,  Vahe Gharakhanyan ,  Julia Yang ,  Alexander Holiday

IPC: C08J11/16

CPC classification number: C08J11/16 ,  C08J2367/04 ,  C08J2367/02 ,  C08J2467/02 ,  C08J2467/04

Abstract: Computer-implemented methods may include accessing a predictive function. The predictive function may be configured to receive a partial or complete bond string and position (BSP) representation of a molecule of a reactant ionic liquid, where the representation identifies relative positions of atoms in the molecule. The predictive function may be configured to predict a reaction-characteristic value that characterizes a reaction between the ionic liquid and a particular polymer. The predictive function may be generated using training data corresponding to a set of molecules that were selected using Bayesian optimization, one or more previous versions of the predictive function, and experimentally derived reaction-characteristic values characterizing reactions between the molecules and the particular polymer. The method may also include identifying a particular ionic liquid as a prospect for depolymerizing the particular polymer based on the predictive function. The method may further include outputting an identification of the ionic liquid.