Advancements in Predicting the Fatigue Lifetime of Structural Adhesive Joints
In the realm of structural engineering, the behavior of structural adhesives stands as a pivotal yet complex enigma.
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.
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.
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 […]