Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning.

The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.

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.