Abstract: In some embodiments, the system is directed to an autonomous inspection system for electrical grid components. In some embodiments, the system collects electrical grid component data using an autonomous drone and then transmits the inspection data to one or more computers. In some embodiments, the system includes artificial intelligence that analysis the data and identifies electrical grid components defects and provides a model highlighting the defects to a user. In some embodiments, the system enables a user to train the artificial intelligence by providing feedback for models where defects or components are not properly identified.

Abstract: A device generates a capability map. The device receives one or more design spaces from a materials supplier, the one or more design spaces including candidate components and capabilities of tools available to the materials supplier. The device inputs a design space of the one or more design spaces into a machine learning model, the training data including a plurality of components including input materials and/or chemicals, and, for respective combinations of the plurality of components, a plurality of respective performance properties. The device receives as output from the model a capability map of the materials supplier storing possible combinations of performance properties and a respective difficulty of developing a composition with that combination of performance properties. The device outputs a user interface for display to a user indicating data of the capability map.

Abstract: A device generates a capability map. The device receives one or more design spaces from a materials supplier, the one or more design spaces including candidate components and capabilities of tools available to the materials supplier. The device inputs a design space of the one or more design spaces into a machine learning model, the training data including a plurality of components including input materials and/or chemicals, and, for respective combinations of the plurality of components, a plurality of respective performance properties. The device receives as output from the model a capability map of the materials supplier storing possible combinations of performance properties and a respective difficulty of developing a composition with that combination of performance properties. The device outputs a user interface for display to a user indicating data of the capability map.

Abstract: A system and a method are disclosed for predicting design space quality for materials development and manufacture. In an embodiment, a processor receives input of a material property and a design space. The processor identifies a best data point. For each respective candidate material of the design space, the processor receives, as output from a model, a respective property value. The processor determines respective property values that exceed the property value of the best data point adds them to a subset of candidate materials. The processor determines a PFIC score for candidates in the subset. The processor generates a plurality of curves, each reflecting a respective probability distribution of property values. The processor determines a CMLI score based on the plurality of respective curves. The processor determines that the design space is high quality based on the PFIC and CMLI scores, and outputs a recommendation to proceed.

Abstract: A system and a method are disclosed for predicting design space quality for materials development and manufacture. In an embodiment, a processor receives input of a material property and a design space. The processor identifies a best data point. For each respective candidate material of the design space, the processor receives, as output from a model, a respective property value. The processor determines respective property values that exceed the property value of the best data point adds them to a subset of candidate materials. The processor determines a PFIC score for candidates in the subset. The processor generates a plurality of curves, each reflecting a respective probability distribution of property values. The processor determines a CMLI score based on the plurality of respective curves. The processor determines that the design space is high quality based on the PFIC and CMLI scores, and outputs a recommendation to proceed.