Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive device parameters from molecular properties computed through first principles. By analyzing the quantum chemical properties of diverse dimeric conformers, we estimate the relative probabilities of microscopic processes such as charge separation, recombination, and transport along with charge transfer state formation in the active layer of OPVs. These probabilities set up a random walk of charge carriers in a grid with disordered molecular sites, allowing us to track their average behavior and calculate key device parameters. Our model consistently predicts measured device parameters, including the short-circuit current and open-circuit voltage, for OPVs with diverse NFAs with high accuracy. Additionally, we applied the model to evaluate donor-acceptor combinations of known compounds and newly designed NFA molecules, identifying high-performing pairs. This model offers a computationally efficient approach for designing novel NFA molecules and optimizing the OPV performance.

中文翻译：

---

从分子到器件：评估有机光伏的多尺度方法。


由于其高效的分子设计，非富勒烯受体 （NFA） 具有非常先进的有机光伏 （OPV）。然而，缺乏根据所得设备性能筛选和评估候选 NFA 的模型阻碍了高性能分子的快速开发。这项工作介绍了一个计算框架，利用动力学蒙特卡洛 （kMC） 模型，从通过第一性原理计算的分子特性中推导出器件参数。通过分析各种二聚体构象异构体的量子化学性质，我们估计了 OPV 活性层中电荷分离、复合和传输等微观过程以及电荷转移态形成的相对概率。这些概率在具有无序分子位点的网格中建立了电荷载流子的随机游走，使我们能够跟踪它们的平均行为并计算关键器件参数。我们的模型始终如一地预测具有不同 NFA 的 OPV 的测量器件参数，包括短路电流和开路电压，且精度很高。此外，我们应用该模型来评估已知化合物和新设计的 NFA 分子的供体-受体组合，确定高性能对。该模型为设计新型 NFA 分子和优化 OPV 性能提供了一种计算高效的方法。