  The design of molecules and materials with tailored properties is
challenging, as candidate molecules must satisfy multiple competing
requirements that are often difficult to measure or compute. While molecular
structures, produced through generative deep learning, will satisfy those
patterns, they often only possess specific target properties by chance and not
by design, which makes molecular discovery via this route inefficient. In this
work, we predict molecules with (pareto)-optimal properties by combining a
generative deep learning model that predicts three dimensional conformations of
molecules with a supervised deep learning model that takes these as inputs and
predicts their electronic structure. Optimization of (multiple) molecular
properties is achieved by screening newly generated molecules for desirable
electronic properties and reusing hit molecules to retrain the generative model
with a bias. The approach is demonstrated to find optimal molecules for organic
electronics applications. Our method is generally applicable and eliminates the
need for quantum chemical calculations during predictions, making it suitable
for high-throughput screening in materials and catalyst design.
