利用机器学习分析高效钙钛矿太阳能电池

Naman Shukla, K. A. Kumar, Madhu Allalla, S. Tiwari
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引用次数: 0

摘要

经济实惠的制造以及光伏技术中高效的钙钛矿太阳能电池引起了大家的关注。钙钛矿在太阳能电池中起主导作用,具有吸收系数高、激子结合能低、载流子迁移率高、在合适的能带上具有更大的扩散长度和可用性等特点。机器学习技术的应用被证明是一个福音,可以确保光伏设备的不同性能,设计,简单的施工过程和低成本的最佳实现。机器学习是人工智能的一个分支,它包括大数据聚合、精确结构属性安装、演示和模型验证后的最终模型。数据库的大部分来源是模拟和实验结果、计算和相关文献综述,其中全面汇编了混合钙钛矿器件的性能,收集了元件的结构和性质。结构-属性关系安装属于特征工程,它在结构和属性之间建立了明确的关系。在其他演示过程中,选择合适的算法,生成和测试数据,并取纯估计值。本文详细讨论了机器学习技术在构建高性能钙钛矿太阳能电池中的应用。通过机器学习正确选择和设计活性钙钛矿吸收层,成功地建立了包括无毒(无铅)和稳定性等其他部分的结果。在钙钛矿太阳能技术领域,成熟的机器学习技术成为确定混合钙钛矿溶剂组合和估计整个太阳能电池设计的重要方法,以确保最佳实施。最后,简要讨论了一个分阶段的概念,以应对机器学习的挑战和与流行相关的潜在未来兼容性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of High Efficient Perovskite Solar Cells Using Machine Learning
Affordable manufacturing along with high efficiency perovskite solar cell in photovoltaic technology has everyone's attention. Perovskite, which is in the lead role in solar cells, is full of characteristics such as high absorption coefficient, low exciton binding energy, charge carrier capable of having better mobility as well as more diffusion length and availability in suitable energy band. The application of machine learning technology is proving to be a boon to ensure optimum implementation with different properties in photovoltaic device, design, simple construction process and low-cost price. Machine learning is a branch of artificial intelligence which includes large data aggregation, precise structure property installation, demonstration and final model after model validation. The most of the source of database is the simulation and experimental results, calculations and related literature surveys which have a comprehensive compilation of the performance of hybrid perovskite device, collection of structures and properties of elements. Structure-property relationship installation comes under feature engineering which establishes a clear relationship between structure and the properties. In other demonstration process, proper algorithms are selected, data is generated and tested as well as pure estimated values are taken. This article contains a detailed discussion on the involvement of machine learning technology to build high-performance Perovskite solar cells. Proper selection as well as designing of active perovskite absorbent layer by machine learning successfully establishes results by including other parts such as non-toxic (lead free) and stability. Mature machine learning technology becomes a very essential method in determining the solvent combination of hybrid perovskite and in estimating design of the entire solar cell to ensure optimum implementation in the sector of perovskite solar technology. Finally, a phased concept has been briefly discussed to meet the challenges of machine learning and potential future compatibilities related to the prevalence.
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