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