The solar–to–chemical energy conversion of Earth-abundant resources like water or greenhouse gas pollutants like CO2 promises an alternate energy source that is clean, renewable, and environmentally friendly. The eventual large-scale application of such photo-based energy conversion devices can be realized through the discovery of novel photocatalytic materials that are efficient, selective, and robust. In the past decade, the Materials Genome Initiative has led to a major leap in the development of materials databases, both computational and experimental. Hundreds of photocatalysts have recently been discovered for various chemical reactions, such as water splitting and carbon dioxide reduction, employing these databases and/or data informatics, machine learning, and high-throughput computational and experimental methods. In this article, we review these data-driven photocatalyst discoveries, emphasizing the methods and techniques developed in the last few years to determine the (photo)electrochemical stability of photocatalysts, leading to the discovery of photocatalysts that remain robust and durable under operational conditions.

中文翻译：

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数据驱动的光催化能量转换稳健材料的发现


将地球上丰富的资源（如水）或温室气体污染物（如 CO2）转化为化学能，有望成为清洁、可再生和环保的替代能源。这种基于光的能量转换装置的最终大规模应用可以通过发现高效、选择性和稳健的新型光催化材料来实现。在过去十年中，材料基因组计划在计算和实验材料数据库的发展方面取得了重大飞跃。最近发现了数百种用于各种化学反应的光催化剂，例如水分解和二氧化碳还原，采用这些数据库和/或数据信息学、机器学习以及高通量计算和实验方法。在本文中，我们回顾了这些数据驱动的光催化剂发现，强调了过去几年开发的确定光催化剂（光）电化学稳定性的方法和技术，从而发现了在操作条件下保持稳健和耐用的光催化剂。