Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536),
we were successful in predicting
accurate atomization energies of organic molecules using machine learning
(ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared
to the G4MP2 method. In this work, we extend the use of these ML models
to adiabatic ionization potentials on data sets of energies generated
using quantum chemical calculations. Atomic specific corrections that
were found to improve atomization energies from quantum chemical calculations
have also been used in this study to improve ionization potentials.
The quantum chemical calculations were performed on 3405 molecules
containing eight or fewer non-hydrogen atoms derived from the QM9
data set, using the B3LYP functional with the 6–31G(2df,p)
basis set for optimization. Low-fidelity IPs for these structures
were obtained using two density functional methods: B3LYP/6–31+G(2df,p)
and ωB97XD/6–311+G(3df,2p). Highly accurate G4MP2 calculations
were performed on these optimized structures to obtain high-fidelity
IPs to use in ML models based on the low-fidelity IPs. Our best performing
ML methods gave IPs of organic molecules within a mean absolute deviation
of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates
that ML predictions assisted by quantum chemical calculations can
be used to successfully predict IPs of organic molecules for use in
high throughput screening.