Neural network analysis of molecular hyperpolarisabilities

A standard back‐propagation neural netwrok is trained to predict the hyperpolarisability β of substituted nitrobenzenses as reported from EFISH experiments. Learning is faster with ¹³C NMR chemical shifts as input than with standard substituent constants and the predictions are somewhat better. The dipole moments μ can be predicted at the same time as β, but training to high precision is then much slower. Developments of this approach may be useful in screening out molecules of high β for synthesis and experimental study.