Depolymerization optimization platform

A system comprising:

• one or more data processors;
• and
• a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations comprising a method comprising:
• accessing a predictive function configured to:
• receive a partial or complete bond string and position (BSP) representation of a molecule of a reactant ionic liquid, wherein the partial or complete BSP representation identifies relative positions of atoms connected by a bond in the molecule;
• transform the partial or complete BSP representations into machine-learned molecular fingerprints by applying a graph neural network;
• predict a reaction-characteristic value that characterizes a reaction between the reactant ionic liquid and a particular polymer based on the received partial or complete BSP representation,
• wherein the predictive function was generated at least in part using:
• training data corresponding to a set of molecules that were selected using Bayesian optimization, the training data being associated with the partial or complete BSP representations represented in a reduced dimensionality space by using one or more dimensionality reduction technique,
• one or more previous versions of the predictive function,
• experimentally derived reaction-characteristic values characterizing reactions between the molecules selected using Bayesian optimization and the particular polymer, and
• the machine-learned molecular fingerprints of BSP molecular representation;
• identifying, in the reduced dimensionality space, at least one particular ionic liquid as a prospect for depolymerizing the particular polymer based on the predictive function;
• and
• outputting an identification of the at least one particular ionic liquid.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a method comprising:

• accessing a predictive function configured to:
• receive a partial or complete bond string and position (BSP) representation of a molecule of a reactant ionic liquid, wherein the partial or complete BSP representation identifies relative positions of atoms connected by a bond in the molecule;
• transform the partial or complete BSP representations into machine-learned molecular fingerprints by applying a graph neural network,
• predict a reaction-characteristic value that characterizes a reaction between the reactant ionic liquid and a particular polymer based on the received partial or complete BSP representation;
• wherein the predictive function was generated at least in part using:
• training data corresponding to a set of molecules that were selected using Bayesian optimization, the training data being associated with the partial or complete BSP representations represented in a reduced dimensionality space by using one or more dimensionality reduction technique,
• one or more previous versions of the predictive function,
• experimentally derived reaction-characteristic values characterizing reactions between the molecules selected using Bayesian optimization and the particular polymer, and
• the machine-learned molecular fingerprints of BSP molecular representation;
• identifying, in the reduced dimensionality space, at least one particular ionic liquid as a prospect for depolymerizing the particular polymer based on the predictive function;
• and
• outputting an identification of the at least one particular ionic liquid.