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The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or the coefficient of determination...

Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art model accuracy, researchers are employing increasingly complex machine learning algorithms that often outperform simple regressions in interpolative...

Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in sequential and reinforcement learning. For many such problems in engineering and the physical sciences,...

In this webinar, Dr. Jake Mohin will explore how AI & Materials Informatics can be applied to this domain to accelerate cell testing efforts in the face of high...

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,...

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.

In this panel discussion, Citrine Informatics’ Director of People Operations, Ify Eva Edwards, sits down with other Diversity, Equity, and Belonging leaders to discuss the impact of solidarity.

In this webinar, we will explore how AI & Materials Informatics can be applied to this domain to accelerate reformulation efforts in the face of changing raw materials supply.

Dr. Lenore Kubie passes on advice around data strategy.