Prediction of energy conversion efficiency of organic solar cells based on deep learning

A language-like descriptor for organic compounds was used to describe 29000 organic solar cell donor molecules collected from the Harvard Clean Energy Project Database (CEPDB). Inspired by the similarity between organic chemistry and natural language, these molecules were decomposed into fragments (words) based on the nearest neighbor subgraph theory, and these fragments were arranged into a certain sequence (sentences) by the breadth first search algorithm. After the information of each fragment was embedded into a numerical vector, each molecule can be represented by an information matrix. This matrix is a descriptor called g-FSI, which can reflect the composition and structure information of molecules. The descriptor was then parsed by a deep neural network to extract the embedded information and correlate to the corresponding PCE. The prediction model has obtained the prediction result in which the determination coefficient (R2) is 0.97 and the mean square error (MSE) is 0.16. Compared with the existing research, this model is competitive in accuracy of prediction. The attention mechanism is introduced in the modeling process, and several molecular fragments that are decisive for the PCE value are identified, which can provide guidance information for the reverse design of organic photovoltaic materials. © 2021, Editorial Board of CIESC Journal. All right reserved.