Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and
artificial intelligence with conventional methods in optimization and material synthesis can guide
experimental researchers to develop, design, predict and discover high-performance materials for
photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is
crucial to establish a relationship between photovoltaic properties and chemical structures before
performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is
desirable for designing better organic semiconductor photodetectors. Herein, we first collected large
sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in
the literature. In addition, molecular descriptors that make it easy and fast to predict the required
properties were used to train machine learning models. Power conversion efficiency and energy
levels were also predicted. Multiple models were trained using experimental data. The light gradient
boosting machine (LGBM) regression model and Hist gradient booting regression model are the
best models. The best models were further tuned to achieve better prediction ability. The reliability
of our designed approach was further verified by mining the photovoltaic database to search for
new building units. The results revealed that good consistency is obtained between experimental
outcomes and model predictions, indicating that machine learning is a powerful approach to predict
the properties of photodetectors, which can facilitate their rapid development in various fields.