Small organic conjugated molecules are key elements for low-cost photovoltaic devices. One example is cyanopyridone molecules. By modifying these molecules, for instance through optimally chosen functional groups attached to the backbone, their properties can be improved. However, the very large number of possible modifications makes it difficult to identify the best performing molecules. In the present work, we have used a computational inverse–design approach (PooMa) to identify the positions and types of functional groups attached to a modified cyanopyridone that lead to the best performance in solar-energy harvesting. A QSPR model based on five electronic descriptors has been used to determine the properties of solar cells. Our approach uses a genetic algorithm to search the chemical space containing 18⁴ (104,976) substituted cyanopyridone systems and predicts out of those the best 20 molecules with optimal performance efficiencies (PCE). PooMa uses the Density–Functional Tight-Binding (DFTB) method for calculating the electronic properties. DFTB is a fast method with acceptable accuracy and, therefore, can be used on a normal desktop without expensive hard- or software. In order to get further information about our suggested systems, a DFT method and its derivative TD-DFT are applied.

有机共轭小分子是低成本光伏设备的关键要素。一个例子是氰基吡啶酮分子。通过修饰这些分子，例如通过连接至主链的最佳选择的官能团，可以改善其性能。然而，大量可能的修饰使得难以鉴定表现最佳的分子。在目前的工作中，我们使用了计算逆设计方法（PooMa）来确定与修饰的氰基吡啶酮相连的官能团的位置和类型，这些官能团可导致太阳能收集中的最佳性能。基于五个电子描述符的QSPR模型已用于确定太阳能电池的属性。我们的方法使用遗传算法搜索包含18 ⁴的化学空间（104,976）取代了氰基吡啶酮系统，并从那些具有最佳性能效率（PCE）的最佳20种分子中进行了预测。PooMa使用密度-功能紧密绑定（DFTB）方法来计算电子性能。DFTB是一种具有可接受的准确性的快速方法，因此可以在没有昂贵的硬件或软件的情况下用于普通台式机。为了获得有关我们建议的系统的更多信息，应用了DFT方法及其派生的TD-DFT。