Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films
Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films
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DOI：
10.1016/j.commatsci.2020.110119
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发表时间：
2021-02
影响因子：
3.3
通讯作者：
Joydeep Munshi;Wei Chen;T. Chien;G. Balasubramanian
中科院分区：
文献类型：
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作者：
Joydeep Munshi;Wei Chen;T. Chien;G. Balasubramanian
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，该优化技术与粗粒度分子动力学(CGMD)模拟相结合来解决有机光伏(OPV)器件的材料和工艺设计问题。利用该方法优化了由聚(3-己基噻吩基)(P3HT)和苯基-C61-丁酸甲酯(PCBM)组成的聚合物共混有源层的给体和受体材料的组成以及形态演化过程中的热退火温度，以提高功率转换效率(PCE)。最优解与先前的实验定性一致，确定了有助于提高材料性能的设计变量之间的相关性。该框架被扩展到多目标设计(MOCS-CGMD)，以达到共混物形态的帕累托最优，同时提高激子扩散到电荷输运概率和材料的极限拉伸强度。结果表明，较高的退火温度提高了激子扩散到电荷输运的几率，而PCBM质量分数在0.4~0.6之间增加了潜在共混形态的拉伸强度。
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.