Citrine Informatics, Inc.

Edward Soo Kim

Julia Black Ling

Erin Melissa Tan Antono

Ryan Michael Latture

Sean Phillip Paradiso

Yoolhee Kim

Maxwell Lipford Hutchinson

Bryce William Meredig

A system and a method are disclosed that, in an embodiment, receive first input from a user of a candidate formulation recipe, and second input from the user of target properties and target property constraints. The system inputs the first input into a machine learning model, the model having been trained using historical training data, each element of the historical training data corresponding to a known formulation having a known feature representation, each known formulation having associated properties and statistical representations of each feature of the known formulation that form the known feature representation. The system receives as output from the model a predicted property of a candidate formulation derived using the first input and the likelihood that the candidate formulation satisfies the target property constraints using the second input. The system generates for display to the user a predicted likelihood that the predicted property satisfies the second input.

Using machine learning to explore formulations recipes with new ingredients

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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Medicine Hat, Canada

Edward Soo Kim