A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

Aspects of the disclosure relate to automated error processing. A computing platform may receive historical error/solution information. The computing platform may train, using the historical error/solution information, an artificial intelligence engine to automatically identify solutions for current errors for a plurality of users. The computing platform may identify current errors for a user of the plurality of users. The computing platform may notify the user of the current errors. The computing platform may receive a request to correct an error of the one or more current errors. The computing platform may identify, using the artificial intelligence engine, a solution to the error. The computing platform may automatically perform actions to achieve the solution. The computing platform may send, after performing the actions, commands directing an event processing system to process an event with which the error was associated, which may cause the event processing system to process the event.

Aspects of the disclosure relate to automated error processing. A computing platform may receive historical error/solution information. The computing platform may train, using the historical error/solution information, an artificial intelligence engine to automatically identify solutions for current errors for a plurality of users. The computing platform may identify current errors for a user of the plurality of users. The computing platform may notify the user of the current errors. The computing platform may receive a request to correct an error of the one or more current errors. The computing platform may identify, using the artificial intelligence engine, a solution to the error. The computing platform may automatically perform actions to achieve the solution. The computing platform may send, after performing the actions, commands directing an event processing system to process an event with which the error was associated, which may cause the event processing system to process the event.

A machine learning model is trained based at least on previous change requests, wherein each of the previous change requests are associated with a controlled management of a lifecycle of a change to an information technology environment. A security vulnerability of the information technology environment is identified. Using the trained machine learning model, a corresponding match score for each of a plurality of pending change requests is determined for the security vulnerability. An indication of whether a resolution specification for the security vulnerability is to be linked with one of the plurality of pending change requests selected based on a factor associated with its corresponding match score is received.

A machine learning model is trained based at least on previous change requests, wherein each of the previous change requests are associated with a controlled management of a lifecycle of a change to an information technology environment. A security vulnerability of the information technology environment is identified. Using the trained machine learning model, a corresponding match score for each of a plurality of pending change requests is determined for the security vulnerability. An indication of whether a resolution specification for the security vulnerability is to be linked with one of the plurality of pending change requests selected based on a factor associated with its corresponding match score is received.

Systems and methods for automatically building a machine learning model are disclosed. A plurality of variables is displayed via a graphical user interface (GUI). A target variable and a first independent variable are identified from the plurality of variables. A parameter associated with the machine learning model is identified. Collected data is received via the GUI. A first machine learning model is built using as inputs, the parameter and the collected data associated with the first independent variable and the target variable. A change is made to at least a portion of the inputs used to build the first machine learning model. A second machine learning model is built based on the change. A prediction accuracy of the first machine learning model is compared to the prediction accuracy of the second machine learning model. Either the first or second machine learning model is selected based on the prediction accuracy.

Various embodiments relate to network capabilities. Devices of a network can have different capabilities. The network can provide artificial intelligence (AI) enabled, machine learning (ML) enabled, deep learning (DL) enabled networked access to these capabilities. The capabilities can share a common AI/ML/DL-enabled open layer-based net-centric logical protocol architecture. Also, different features can be achieved through different layers. As an example, AI enabled access can be achieved through the application layer, ML enabled access and DL enabled access can be achieved through the presentation layer and the session layer, and network access is achieved through the transport layer, the network layer, the link layer, and the physical layer.

Various embodiments relate to network capabilities. Devices of a network can have different capabilities. The network can provide artificial intelligence (AI) enabled, machine learning (ML) enabled, deep learning (DL) enabled networked access to these capabilities. The capabilities can share a common AI/ML/DL-enabled open layer-based net-centric logical protocol architecture. Also, different features can be achieved through different layers. As an example, AI enabled access can be achieved through the application layer, ML enabled access and DL enabled access can be achieved through the presentation layer and the session layer, and network access is achieved through the transport layer, the network layer, the link layer, and the physical layer.

User-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. Individual users may train the model using the local, private dataset to generate one or more parameter updates. Prior to sending the generated parameter updates to the aggregation server for incorporation into the machine learning model, a user may modify the parameter updates by applying respective noise values to individual ones of the parameter updates to ensure differential privacy for the dataset private to the user. The aggregation server may then receive the respective modified parameter updates from the multiple users and aggregate the updates into a single set of parameter updates to update the machine learning model. The federated machine learning may further include iteratively performing said sending, training, modifying, receiving, aggregating and updating steps.