Accelerating amine-based CO2 capture with machine learning: From molecular screening to process optimization

Amine-based CO 2 capture represents the most mature approach for large-scale carbon reduction, with systems implemented across multiple industrial demonstration projects globally. However, vast chemical spaces encompassing millions of potential formulations and complex multiscale coupling effects pose unprecedented challenges for traditional experimental methods. Machine learning applications have achieved revolutionary advances through differentiated strategies. In liquid amine systems, ensemble learning algorithms delivered breakthrough precision improvements from traditional 4-5% to below 0.93%, while interpretable models revealed that nitrogen atom charge distribution contributes 56% to reaction barriers,…

• NN
  National Natural Science Foundation of China

  Awards: 52225003, 52570117
• CN
  China National Funds for Distinguished Young Scientists

  Award: 52225003