Materials acceleration platforms (MAPs) combine automation and artificial intelligence to accelerate the discovery of molecules and materials. They have potential to play a role in addressing complex societal problems such as climate change. Solar chemicals and fuels generation via heterogeneous CO₂ photo(thermal)catalysis is a relatively unexplored process that holds potential for contributing toward an environmentally and economically sustainable future and is therefore a very promising application for MAP science and engineering. Here, we present a brief overview of how design and innovation in heterogeneous CO₂ photo(thermal)catalysis, from materials discovery to engineering and scaleup, could benefit from MAPs. We discuss relevant design and performance descriptors and the level of automation of state-of-the-art experimental techniques, and we review examples of artificial intelligence in data analysis. Based on these precedents, we finally propose a MAP outline for autonomous and accelerated discoveries in the emerging field of solar chemicals and fuels sourced from CO₂ photo(thermal)catalysis.

材料加速平台 (MAP) 结合了自动化和人工智能，以加速分子和材料的发现。它们有潜力在解决气候变化等复杂的社会问题方面发挥作用。通过非均相 CO ₂光（热）催化产生太阳能化学品和燃料是一个相对未开发的过程，它具有为环境和经济可持续发展的未来做出贡献的潜力，因此是 MAP 科学和工程的一个非常有前途的应用。在这里，我们简要概述了异构 CO _{2中的设计和创新}光（热）催化，从材料发现到工程和放大，都可以从 MAP 中受益。我们讨论了相关的设计和性能描述符以及最先进实验技术的自动化水平，并回顾了数据分析中人工智能的示例。基于这些先例，我们最终提出了一个 MAP 大纲，用于在源自 CO 2_光（热）催化的太阳能化学品和燃料的新兴领域中自主和加速发现。