Autonomous electrocatalyst discovery for sustainable nitrogen reduction
Sequential learning and autonomous atomistic simulations help discover new catalysts with co-optimized properties.
Closed-loop fully-automated frameworks for accelerating materials discovery
How much can a combination of sequential learning and closed-loop automation accelerate materials discovery?
Quantifying the Performance of Machine Learning Models in 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 accelerates customer tailored composite material design
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.
Getting Data Ready for AI
Dr. Lenore Kubie passes on advice around data strategy.
Data Scientists – a hot commodity in tech
Steve Edkins, Citrine’s Data Science Manager, speaks about working at Citrine.
Materials Informatics Platform – Build Versus Buy
Companies are thinking through the build versus buy decision on Materials Informatics platforms. This blog outlines what to think about.
Why I Now Care About What Goes Into Everyday Products – Brooke Anderson
Account Executive Brooke Anderson writes an opinion piece about sustainability.
Life At Citrine – Erin Antono
Hear from Erin Antono as she describes what she likes about working at Citrine.
Materials Informatics and Sustainability – the case for urgency
Materials innovation is essential to sustainability. Materials Informatics can accelerate that. To speed up adoption of MI we need both education and public research to produce FAIR data.