  This paper presents a novel approach for predicting Power Conversion
Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD:
synergizing molecular Graphs and Language Descriptors for enhanced PCE
prediction. Due to the lack of high-quality experimental data, we collect a
dataset consisting of 500 pairs of OPV donor and acceptor molecules along with
their corresponding PCE values, which we utilize as the training data for our
predictive model. In this low-data regime, GLaD leverages properties learned
from large language models (LLMs) pretrained on extensive scientific literature
to enrich molecular structural representations, allowing for a multimodal
representation of molecules. GLaD achieves precise predictions of PCE, thereby
facilitating the synthesis of new OPV molecules with improved efficiency.
Furthermore, GLaD showcases versatility, as it applies to a range of molecular
property prediction tasks (BBBP, BACE, ClinTox, and SIDER), not limited to
those concerning OPV materials. Especially, GLaD proves valuable for tasks in
low-data regimes within the chemical space, as it enriches molecular
representations by incorporating molecular property descriptions learned from
large-scale pretraining. This capability is significant in real-world
scientific endeavors like drug and material discovery, where access to
comprehensive data is crucial for informed decision-making and efficient
exploration of the chemical space.
