Abstract We discuss results from a machine learned (ML) metaheuristic cuckoo search (CS) optimization technique that is coupled with coarse-grained molecular dynamics (CGMD) simulations to solve a materials and processing design problem for organic photovoltaic (OPV) devices. The method is employed to optimize the composition of donor and acceptor materials, and the thermal annealing temperature during the morphological evolution of a polymer blend active layer composed of poly-(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM), for an increased power conversion efficiency (PCE). The optimal solutions, which are in qualitative agreement with earlier experiments, identify correlation between the design variables that contributes to an enhanced material performance. The framework is extended to multi-objective design (MOCS-CGMD) to attain a Pareto optimality for the blend morphology, and enhance concurrently the exciton diffusion to charge transport probability and the ultimate tensile strength of the material. The predictions reveal that a higher annealing temperature enhances the exciton diffusion to charge transport probability, while a PCBM weight fraction between 0.4 and 0.6 increases the tensile strength of the underlying blend morphology.

中文翻译：

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P3HT：PCBM薄膜体异质结形态的机器学习元启发式优化

摘要 我们讨论了机器学习 (ML) 元启发式布谷鸟搜索 (CS) 优化技术的结果，该技术与粗粒度分子动力学 (CGMD) 模拟相结合，以解决有机光伏 (OPV) 器件的材料和加工设计问题。该方法用于优化供体和受体材料的组成，以及由聚（3-己基噻吩）（P3HT）和苯基-C61-丁酸甲酯组成的聚合物共混活性层在形态演变过程中的热退火温度(PCBM)，以提高功率转换效率 (PCE)。与早期实验定性一致的最佳解决方案确定了有助于提高材料性能的设计变量之间的相关性。该框架扩展到多目标设计（MOCS-CGMD），以获得混合形态的帕累托最优，并同时增强激子扩散到电荷传输概率和材料的极限拉伸强度。预测表明，较高的退火温度会增强激子扩散到电荷传输的可能性，而介于 0.4 和 0.6 之间的 PCBM 重量分数会增加底层共混物形态的拉伸强度。