
The OCx24 project leverages a large-scale experimental dataset and computational modeling to bridge the gap between theoretical predictions and real-world performance of catalysts for green hydrogen production and CO2 upcycling.


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open-catalyst-experiments-2024-ocx24-a-dataset-and-computational-analysis-for-bridging-the-gap-between-predicted-and-experimental-catalyst-performance-for-green-hydrogen-production-and-co2-upcycling


Open Catalyst Experiments 2024 (OCx24): A Dataset and Computational Analysis for Bridging the Gap Between Predicted and Experimental Catalyst Performance for Green Hydrogen Production and CO2 Upcycling


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This research demonstrates a novel approach to catalyst discovery by combining Hammett σ constants with machine learning, specifically for the Suzuki-Miyaura cross-coupling reaction, leading to the identification of promising, cost-effective catalyst candidates.


combining-hammett-σ-constants-for-improved-machine-learning-and-catalyst-discovery-in-homogeneous-organometallic-catalysis


Combining Hammett σ Constants for Improved Machine Learning and Catalyst Discovery in Homogeneous Organometallic Catalysis
