多相催化在各种大宗化学品制造与能源转换过程中持续占据核心位置。这些过程的革新亟需探索具备理想催化活性与经济可行性的新材料。为应对此挑战，基于理论计算的高通量筛选提供了一条可行之路。机器学习通过利用相对低成本信息实现对表面活性的准确预测，在加速上述筛选进程中展现出了巨大潜力。鉴于表面吸附能显著地衡量了固体催化剂的催化潜力，本综述聚焦于机器学习在表面吸附能预测领域的最新进展。依据模型输入特征和复杂程度，机器学习模型得以进行分类及讨论。最后，本文对基于机器学习的催化剂筛选方法所面临的当前挑战和未来机会进行了展望。我们相信，本综述不仅凝练了采用机器学习加速催化剂发现的主要进展，更启发了研者发展新策略以加速材料设计，最终有望重塑化工与能源图景。

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.