  Machine learning methods have shown promise in predicting molecular
properties, and given sufficient training data machine learning approaches can
enable rapid high-throughput virtual screening of large libraries of compounds.
Graph-based neural network architectures have emerged in recent years as the
most successful approach for predictions based on molecular structure, and have
consistently achieved the best performance on benchmark quantum chemical
datasets. However, these models have typically required optimized 3D structural
information for the molecule to achieve the highest accuracy. These 3D
geometries are costly to compute for high levels of theory, limiting the
applicability and practicality of machine learning methods in high-throughput
screening applications. In this study, we present a new database of candidate
molecules for organic photovoltaic applications, comprising approximately
91,000 unique chemical structures.Compared to existing datasets, this dataset
contains substantially larger molecules (up to 200 atoms) as well as
extrapolated properties for long polymer chains. We show that message-passing
neural networks trained with and without 3D structural information for these
molecules achieve similar accuracy, comparable to state-of-the-art methods on
existing benchmark datasets. These results therefore emphasize that for larger
molecules with practical applications, near-optimal prediction results can be
obtained without using optimized 3D geometry as an input. We further show that
learned molecular representations can be leveraged to reduce the training data
required to transfer predictions to a new DFT functional.
