  Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions)
when exposed to light. These compounds are critical components of the
photolithography processes that are used in the manufacture of semiconductor
logic and memory chips. The exponential increase in the demand for
semiconductors has highlighted the need for discovering novel photo-acid
generators. While de novo molecule design using deep generative models has been
widely employed for drug discovery and material design, its application to the
creation of novel photo-acid generators poses several unique challenges, such
as lack of property labels. In this paper, we highlight these challenges and
propose a generative modeling approach that utilizes conditional generation
from a pre-trained deep autoencoder and expert-in-the-loop techniques. The
validity of the proposed approach was evaluated with the help of subject matter
experts, indicating the promise of such an approach for applications beyond the
creation of novel photo-acid generators.
