Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge
transfer
and other optoelectronic properties of Lewis acid adducts remains
elusive. This challenge must be addressed through complementary methods
combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can
be computationally expensive and less straightforward than those sufficient
for simple ground-state properties, especially for adducts of large
conjugated molecules and Lewis acids. In this contribution, we show
that machine learning (ML) can accurately predict density functional
theory (DFT)-calculated charge transfer and even properties associated
with excited states of adducts from readily obtained molecular descriptors.
Seven ML models, built from a dataset of over 1000 adducts, show exceptional
performance in predicting charge transfer and other optoelectronic
properties with a Pearson correlation coefficient of up to 0.99. More
importantly, the influence of each molecular descriptor on predicted
properties can be quantitatively evaluated from ML models. This contributes
to the optimization of a priori design of Lewis adducts for future
applications, especially in organic electronics.