  The search for new high-performance organic semiconducting molecules is
challenging due to the vastness of the chemical space, machine learning
methods, particularly deep learning models like graph neural networks (GNNs),
have shown promising potential to address such challenge. However, practical
applications of GNNs for chemistry are often limited by the availability of
labelled data. Meanwhile, unlabelled molecular data is abundant and could
potentially be utilized to alleviate the scarcity issue of labelled data. Here,
we advocate the use of self-supervised learning to improve the performance of
GNNs by pre-training them with unlabeled molecular data. We investigate
regression problems involving ground and excited state properties, both
relevant for optoelectronic properties of organic semiconductors. Additionally,
we extend the self-supervised learning strategy to molecules in non-equilibrium
configurations which are important for studying the effects of disorder. In all
cases, we obtain considerable performance improvement over results without
pre-training, in particular when labelled training data is limited, and such
improvement is attributed to the capability of self-supervised learning in
identifying structural similarity among unlabeled molecules.
