Sequential learning and autonomous atomistic simulations help discover new catalysts with co-optimized properties.
How much can a combination of sequential learning and closed-loop automation accelerate materials discovery?
Our findings illustrate the importance of designing a modeling and discovery workflow which is highly tailored toward the specific problem of interest. In ML and SL for materials discovery, one size does not fit all, traditional error metrics may not be a good predictor of success, and the specifics of the design challenge have important implications for the optimal configuration of materials discovery strategies.
Materials Informatics helps a custom thermoplastic producer rapidly identify custom masterbatch + polymer blends for its customized solutions with far fewer experiments than traditional approaches to product development.
Dr. Lenore Kubie passes on advice around data strategy.
Steve Edkins, Citrine’s Data Science Manager, speaks about working at Citrine.
Read how the Citrine Platform was used to develop new carbon fiber process additives with local ingredients in record time.
Learn how Citrine’s AI Platform was used to rapidly screen 2500+ polymers in just 5 months. See how a new workflow was developed where researchers can now set a target and immediately receive a list of the top 10 polymers most likely to hit the target.
Account Executive Brooke Anderson writes an opinion piece about sustainability.
New electrochemical reactions are needed to reduce the carbon footprint of chemical production. Use AI to find new catalysts.