Predicting hydroformylation regioselectivity from literature data via machine learning

In this issue of Chem Catalysis, Mao et al. develop machine learning models that predict terminal alkene regioselectivity in catalytic hydroformylation, showing that high temperature, low pressure, and low metal concentration favor linear products. These models enable high-throughput screening, potentially advancing innovations in this industrial process.