Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors

Convolutional neural network (CNN) is employed to construct
generative
and prediction models for the design and analysis of non-fullerene
acceptors (NFAs) in organic solar cells. It is demonstrated that the
dilated causal CNN can be trained as a good string-based molecular
generation model, and the diversity of the generated NFAs is influenced
by the depth of convolutional layers. In the property prediction model,
the features of NFAs are extracted from the string representations
by the dilated CNN. Specially, the attention mechanism is adopted
to pool the extracted information, from which the contributions of
fragments to molecular properties can be obtained by calculating the
corresponding weighted sum. The promising NFAs among the predicted
molecules are further verified by quantum chemistry calculations.
The proposed generative, prediction models and the theoretical calculations
perform as a complete cycle from molecular generation and property
prediction to verification, which offer a strategy for the application
of CNN in material discovery.