Light is a form of energy that can be converted to electric and chemical energies. Thus, organic photovoltaics (OPVs), perovskite solar cells (PSCs), photocatalysts, and photodetectors have evolved as scientific and commercial enterprises. However, the complex photochemical reactions and multicomponent materials involved in these systems have hampered rapid progress in their fundamental understanding and material design. This review showcases the evaluation-oriented exploration of photo energy conversion materials by using electrodeless time-resolved microwave conductivity (TRMC) and materials informatics (MI). TRMC with its unique options (excitation sources, environmental control, frequency modulation, etc.) provides not only accelerated experimental screening of OPV and PSC materials but also a versatile route toward shedding light on their charge carrier dynamics. Furthermore, MI powered by machine learning is shown to allow extremely high-throughput exploration in the large molecular space, which is compatible with experimental screening and combinatorial synthesis. This article reviews an evaluation-oriented exploration of photo energy conversion systems including organic photovoltaics, perovskite solar cells, photocatalysts, and photodetectors. A time-resolved spectroscopy using a gigahertz electromagnetic wave enables rapid screening of potential optoelectronics of organic/inorganic semiconductors and fast finding of their optimal film processing conditions. This approach is further empowered by machine learning that provides a high-throughput virtual screening in the large molecular space. The author discusses a perspective on this evaluation (from fundamental to application) and its effective combination with data science.

中文翻译：

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面向评价的光能转换系统探索：从基础光电子学和材料筛选到与数据科学的结合

光是一种可以转化为电能和化学能的能量形式。因此，有机光伏（OPV）、钙钛矿太阳能电池（PSC）、光催化剂和光电探测器已经发展成为科学和商业企业。然而，这些系统中涉及的复杂光化学反应和多组分材料阻碍了其基本理解和材料设计的快速进展。本综述展示了利用无电极时间分辨微波电导率 (TRMC) 和材料信息学 (MI) 对光能转换材料进行评估导向的探索。TRMC 具有独特的选项（激励源、环境控制、频率调制等）。) 不仅提供了 OPV 和 PSC 材料的加速实验筛选，而且还提供了一条了解其电荷载流子动力学的通用途径。此外，由机器学习驱动的 MI 被证明可以在大分子空间中进行极高通量的探索，这与实验筛选和组合合成兼容。本文回顾了以评估为导向的光能转换系统探索，包括有机光伏、钙钛矿太阳能电池、光催化剂和光电探测器。使用千兆赫兹电磁波的时间分辨光谱能够快速筛选有机/无机半导体的潜在光电子学，并快速找到它们的最佳薄膜加工条件。机器学习进一步增强了这种方法的能力，机器学习在大分子空间中提供了高通量虚拟筛选。作者讨论了这种评估的观点（从基础到应用）及其与数据科学的有效结合。