  Singlet fission has emerged as one of the most exciting phenomena known to
improve the efficiencies of different types of solar cells and has found uses
in diverse optoelectronic applications. The range of available singlet fission
molecules is, however, limited as to undergo singlet fission, molecules have to
satisfy certain energy conditions. Recent advances in material search using
inverse design has enabled the prediction of materials for a wide range of
applications and has emerged as one of the most efficient methods in the
discovery of suitable materials. It is particularly helpful in manipulating
large datasets, uncovering hidden information from the molecular dataset and
generating new structures. However, we seldom encounter large datasets in
structure prediction problems in material science. In our work, we put forward
inverse design of possible singlet fission molecules using a transfer learning
based approach where we make use of a much larger ChEMBL dataset of
structurally similar molecules to transfer the learned characteristics to the
singlet fission dataset.
