Methods for predicting likelihood of successful experimental synthesis of computer-generated materials by combining network analysis and machine learning

A system configured to determine a probability of successful synthesis of a material in the real world at one or more points in time, the system comprising:

• a non-transient electronic storage configured to store a trained classifier model;
• one or more physical processors configured by machine-readable instructions to:
• obtain unsynthesized hypothetical material information, the unsynthesized hypothetical material information defining a set of parameter values for an unsynthesized hypothetical material;
• obtaining the classifier model from the non-transient electronic storage, the classifier model having been trained by a training data set, the training data set include (i) network parameter information and (ii) discovery information;
• determine probabilities of successful synthesis of the unsynthesized hypothetical material at discrete points in time of the period of time by applying the classifier model to the unsynthesized hypothetical material information;
• provide a user with the probabilities of successful synthesis of the unsynthesized hypothetical material at discrete points in time of the period of time on an interface.

A method for determining a probability of successful synthesis of a material in the real world at one or more points in time, the method comprising:

• obtaining material network information defining a network for a set of previously synthesized materials, the material network information including node information, edge information, and discovery information, the node information characterizing previously synthesized materials represented by nodes in the network, the edge information representing connections between the previously synthesized materials based on shared components between individual ones of the previously synthesized materials, wherein the edge information further represents connections between the previously synthesized materials based on other relationship between previously synthesized materials including relationships derived from thermodynamic co-existence information and phase diagrams, the discovery information defining points in time the individual previously synthesized materials in the network were first synthesized in the real world;
• determining network parameter information for the network of the period of time, the network parameter information specifying sets of parameter values of the previously synthesized materials, wherein the sets of parameter values of the previously synthesized materials are determined by representing the previously synthesized materials as individual nodes in the network at discrete points in time over a period of time and determined the sets of parameter values based on the network, wherein the network at a given discrete points in time over the period of time includes the nodes representing the previously synthesized materials that were synthesized by the given discrete points in time, and the previously synthesized materials that were not synthesized by the given discrete points in time are independently introduced in the network to determine the sets of parameter values of the previously synthesized materials that were not synthesized;
• training a classifier model from the individual previously synthesized materials using the discovery information and network parameter information to generate probabilities for materials being successfully synthesized in the real world at the individual discrete points in time;
• determining a probability of an unsynthesized hypothetical material being successfully synthesized in the real world at the individual discrete points in time by applying the classifier model to unsynthesized material information defining the unsynthesized hypothetical material.

A method for determining a probability of successful synthesis of a material in the real world at one or more points in time, the system comprising:

• obtaining unsynthesized hypothetical material information, the unsynthesized hypothetical material information defining a set of parameter values for an unsynthesized hypothetical material;
• obtaining the classifier model from a non-transient electronic storage storing a trained classifier model, the classifier model having been trained by a training data set, the training data set include (i) network parameter information and (ii) discovery information;
• determining probabilities of successful synthesis of the unsynthesized hypothetical material at the discrete points in time of the period of time by applying the classifier model to the unsynthesized hypothetical material information;
• and
• providing a user with the probabilities of successful synthesis of the unsynthesized hypothetical material the discrete points in time of the period of time on an interface.