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Machine Learning-Enhanced Design of Homogeneous Bifunctional Catalysts for CO2 Hydrogenation

CORDIS · observation · Publication date unknown

Machine Learning-Enhanced Design of Homogeneous Bifunctional Catalysts for CO2 Hydrogenation As a global society, we face the urgent challenge of reducing CO2 emissions. In response, governmental organisations, such as the European Union, have introduced policies promoting sustainable practices and renewable energy sources. One initiative is the conversion of CO2 into valuable products, with CO2-based methanol synthesis emerging as a promising approach. Methanol serves both as a low-density fuel and a feedstock for essential chemicals. While heterogeneous catalysts are commonly used in this reaction, they necessitate harsh conditions and exhibit low selectivity. Homogeneous catalysts, in contrast, operate at milder conditions and allow for fine-tuned active sites, potentially enhancing performance. Nevertheless, conventional methods for discovering new efficient catalysts are time-consum

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award

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SIGNED

region

EU

value

251578.56

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EUR