Emerging opportunities for hybrid perovskite solar cells using machine learning
Emerging opportunities for hybrid perovskite solar cells using machine learning
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DOI：
10.1063/5.0146828
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Abigail R. Hering;Mansha Dubey;M. Leite
中科院分区：
文献类型：
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作者：
Abigail R. Hering;Mansha Dubey;M. Leite
虽然在混合有机-无机钙钛矿（HOIP）太阳能电池的生产步骤中存在几个瓶颈，包括成分筛选、制造、材料稳定性和器件性能，但近年来机器学习方法已经开始解决这些问题。在HOIP开发的每一步，都成功地采用了不同的算法来解决独特的问题。具体而言，高通量实验产生了有效实现机器学习方法所需的大量训练数据。在这里，我们概述了机器学习模型，包括线性回归、神经网络、深度学习和统计预测。从文献中，机器学习应用到HOIP组合物筛选，薄膜制造，薄膜表征和完整的设备测试的实验例子进行了讨论。这些范例为HOIP太阳能电池研究的未来提供了见解。随着数据库的扩展和计算能力的提高，对HOIP行为的越来越准确的预测变得可能。
While there are several bottlenecks in hybrid organic–inorganic perovskite (HOIP) solar cell production steps, including composition screening, fabrication, material stability, and device performance, machine learning approaches have begun to tackle each of these issues in recent years. Different algorithms have successfully been adopted to solve the unique problems at each step of HOIP development. Specifically, high-throughput experimentation produces vast amount of training data required to effectively implement machine learning methods. Here, we present an overview of machine learning models, including linear regression, neural networks, deep learning, and statistical forecasting. Experimental examples from the literature, where machine learning is applied to HOIP composition screening, thin film fabrication, thin film characterization, and full device testing, are discussed. These paradigms give insights into the future of HOIP solar cell research. As databases expand and computational power improves, increasingly accurate predictions of the HOIP behavior are becoming possible.