In this paper, the ability of three selected machine learning neural and baseline models in predicting power conversion efficiency (PCE) organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), simple graph networks (simple GNN) well support vector regression (SVR), ...