Machine learning (ML) is emerging as a powerful approach that has recently shown potential to affect various frontiers of carbon capture, a key interim technology to assist in the mitigation of climate change. In this perspective, we reveal how ML implementations have improved this process in many aspects, for both absorption- and adsorption-based approaches, ranging from the molecular to process level. We discuss the role of ML in predicting the thermodynamic properties of absorbents and in improving the absorption process. For adsorption processes, we discuss the promises of ML techniques for exploring many options to find the most cost-effective process scheme, which involves choosing a solid adsorbent and designing a process configuration. We also highlight the advantages of ML and the associated risks, elaborate on the importance of the features needed to train ML models, and identify promising future opportunities for ML in carbon capture processes.

机器学习（ML）作为一种强大的方法正在兴起，最近已显示出潜在的潜力来影响碳捕集的各个领域，这是有助于缓解气候变化的一项关键的临时技术。从这个角度来看，我们揭示了ML实施如何在许多方面改进了这一过程，无论是基于吸收还是基于吸附的方法，从分子水平到过程水平。我们讨论了ML在预测吸收剂的热力学性质和改善吸收过程中的作用。对于吸附工艺，我们讨论了ML技术的前景，以探索许多选择以找到最具成本效益的工艺方案，其中包括选择固体吸附剂并设计工艺配置。我们还强调了机器学习的优势以及相关的风险，