Chemical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Molecules. 2023 Jan 27;28(3):1240. doi: 10.3390/molecules28031240.
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.
由于有机半导体具有很大的多功能性，因此为光电探测器选择合适的（有机半导体）材料是一项具有挑战性的任务。将计算机科学和人工智能与优化和材料合成的传统方法相结合，可以指导实验研究人员开发、设计、预测和发现用于光电探测器的高性能材料。为了找到用于光电探测器的高性能有机半导体材料，在实验室进行合成程序之前，建立光伏性能与化学结构之间的关系至关重要。此外，对能级进行快速预测对于设计更好的有机半导体光电探测器是可取的。在此，我们首先收集了包含文献中报道的有机半导体光电探测器光伏性能的大量数据集。此外，还使用易于快速预测所需性能的分子描述符来训练机器学习模型。还预测了功率转换效率和能级。使用实验数据训练了多个模型。光梯度提升机（LGBM）回归模型和 Hist 梯度提升回归模型是最佳模型。进一步调整最佳模型以获得更好的预测能力。通过挖掘光伏数据库来搜索新的构建单元，进一步验证了我们设计方法的可靠性。结果表明，实验结果与模型预测之间具有良好的一致性，这表明机器学习是一种强大的预测光电探测器性能的方法，可促进其在各个领域的快速发展。
