Global supply chains are facing increasing challenges, highlighting the need for new approaches to material production. One promising avenue involves developing methods to create high-performance materials from low-quality, variable scrap feedstocks.
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.
Using machine learning to create a new open database of corrosion-resistant alloys.
High-temperature alloy design is challenging, but crucial to aeronautical applications. Citrine is working on a project to improve the representation of alloys data and develop techniques to to accurately predict properties outside of the range of input data.
Extracting, cleaning and analyzing and visualizing MPEA data.
New electrochemical reactions are needed to reduce the carbon footprint of chemical production. Use AI to find new catalysts.
Summary of peer reviewed, papers showing experimentally verified predictions of material properties.
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.
