By Alexander Whiteside2021-01-06T14:29:00
Source: © Shutterstock
Best of both worlds for computational organic chemistry predictions
Researchers in the UK and Sweden have combined density functional theory (DFT) and machine learning to develop a method that accurately predicts the kinetics of nucleophilic aromatic substitution reactions. The new method makes accurate predictions for a reaction class that is challenging to approximate with DFT, but doesn’t require the very large data sets that are typical of pure machine learning methods.
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