Database for design of solar cell active layer through genetic algorithm

Microstructure design is a crucial part of developing organic solar cells. Organic solar cells have the potential to become ubiquitous amongst power generation due to their inexpensiveness and ease of fabrication. Although achievements in the chemical properties of the solar cells have been achieved in recent years, a lack of progress in morphology has greatly inhibited organic solar cell adoption. In this, it is illustrated how high-performance microstructures can be developed rapidly via a graph-based strategy. This is in stark contrast to the trial-and-error methods currently employed for organic solar cell microstructure optimization. Treating the microstructure of a material system as graphs allows modular and extensible models that are simple to query and evaluate. The graph surrogate model quickly maps the microstructures properties and integrates well with optimization algorithms while elegantly integrating prior domain knowledge into the microstructure design process. This use of graph-based modeling and probabilistic optimization results in a microstructure design with a 40.29% higher efficiency than conventional solar designs. Fractal analysis was also used to further prove the validity of the designed morphologies. This was accomplished through analyzing models analogous to the function of the solar cell and comparing their similarity with the designed fractal structure. To conclude, graph-based probabilistic optimization led to the identification of a class of microstructures that feature significantly higher efficiencies than currently leading solar cells. It is anticipated that coupling this method with fractal analysis techniques will be widespread for use in optimizing material morphologies. The following dataset includes all code used in microstructure design and fractal analysis, specifically: the creation of a weighted, undirected graph representing the microstructure configuration of chemicals in the solar cell; the approximation of solar cell efficiency through graph-based querying; the optimization of the system through a probabilistic genetic algorithm; and fractal dimension calculator.

微结构设计是开发有机太阳能电池的关键环节。有机太阳能电池因其制造成本低廉、易于加工而具有在发电领域普及的潜力。尽管近年来在太阳能电池的化学性质方面取得了成就，但形态学方面的进展缓慢，极大地阻碍了有机太阳能电池的推广应用。在此，本文展示了如何通过基于图的战略快速开发高性能微结构，这与目前应用于有机太阳能电池微结构优化的试错方法形成鲜明对比。将材料系统的微结构视为图，允许构建模块化和可扩展的模型，这些模型易于查询和评估。基于图的代理模型迅速映射微结构的特性，并与优化算法良好集成，巧妙地将先验领域知识整合到微结构设计过程中。这种基于图的建模和概率优化方法，使得微结构设计效率比传统太阳能设计高出40.29%。此外，通过分析类似于太阳能电池功能的模型，并将它们与设计的分形结构进行比较，也利用分形分析进一步验证了设计形态的有效性。总之，基于图的概率优化导致识别出一种微结构类别，其效率显著高于目前领先的太阳能电池。预计将此方法与分形分析技术相结合，将在优化材料形态学方面得到广泛应用。以下数据集包括用于微结构设计和分形分析的所有代码，具体包括：创建表示太阳能电池中化学物质微结构配置的加权无向图；通过基于图的查询近似太阳能电池效率；通过概率遗传算法优化系统；以及分形维度计算器。