Machine Learning for Solid Ion Conductors

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Redwood City, California
Project Term:
12/22/2015 - 03/21/2017

Technology Description:

The Citrine Informatics team is demonstrating a proof-of-concept for a system that would use experimental work to intelligently guide the investigation of new solid ionic conductor materials. If successful, the project will create a new approach to material discovery generally and new direction for developing promising ionic conductors specifically. The project will aggregate data (both quantitative and meta-data related to experimental conditions) relevant to ionic conductors from the published literature and build advanced, machine learning models for prediction based upon the resulting large database. The team’s system will also experimentally explore the new materials space identified and suggested by the models. The Citrine project could provide researchers near-real-time feedback as they perform experiments, allowing them to dynamically select the most promising research pathways. This would in turn unlock more rapid ionic conductor identification and development, and transform the fields of theoretical and experimental materials science at-large.


ARPA-E Program Director:
Dr. Paul Albertus
Project Contact:
Mr. Gregory Mulholland
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