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Home / Success / Blog / Notes on Successful Projects

Notes on Successful Projects

Closed-loop fully-automated frameworks for accelerating materials discovery

How much can a combination of sequential learning and closed-loop automation accelerate materials discovery?

External Research, Notes on Successful Projects, Blog
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.

External Research, Notes on Successful Projects, Blog
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.

Notes on Successful Projects, Blog
Quantifying Uncertainties in the Materials Digital Twin

Citrine has quantified uncertainty in DFT property calculations as part of a project funded by the US Department of Energy and with collaborators from Olin College and MolSSI.

External Research, Notes on Successful Projects, Blog
Machine Learning and Physics-Based Modeling Hand-in-Hand

Synthesizing and testing new material samples can take months and cost tens of thousands of dollars. Physics-based modeling techniques, such as Density Functional Theory (DFT) are cheaper and quicker. However, these require a large amount of computational power and can still take days. Running a machine learning model takes seconds to minutes and can predict […]

Notes on Successful Projects, Blog

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