Citrine Informatics, Inc.

Pacific Gas and Electric Company

Erin Melissa Tan Antono

Julia Black Ling

Jason Stuart Koeller

Michael S Glass

Teresa Alapat

Patrick Buckles

Khushar Faizan

Jonathan Mello

Michael James Locatelli

Maria Joseph

Marcella Kwan

Edward Soo Kim

Tony Chen

Kunal Datta

Xiwang Li

James Samuel Peerless

Bryce William Meredig

Jacinto Chen

David Grayson

Alexander Willem Anton Van Grootel

Gregory Joseph Mulholland

Jeffrey Mark Lovington

Michael Signorotti

Charlene Chi-Johnston

Guilherme Mattar Bastos

Laura Fehr

Yoolhee Kim

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.

Electric power grid inspection and management system

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.

Product design and materials development integration using a machine learning generated capability map

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.

Predictive design space metrics for materials development

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San Francisco, CA, United States of America

Erin Melissa Tan Antono