Data-driven approaches are currently renovating the field of heterogenous catalysis and open the door to advance catalyst design. Their success depends heavily on the synergy among machine learning (ML), experimental data, and quantum mechanical (QM) calculations. In this brief survey of recent progress, we examine catalysis informatics in the context of (1) ML-aided catalyst characterizations, (2) knowledge extractions from experimental data, (3) predictions of catalytic properties and constructions of reaction networks, and (4) ML-enabled large-scale QM simulations. An outlook on the current challenges of this rapidly evolving field is also provided.

数据驱动的方法目前正在革新多相催化领域，并为推进催化剂设计打开了大门。它们的成功在很大程度上取决于机器学习 (ML)、实验数据和量子力学 (QM) 计算之间的协同作用。在对最近进展的简短调查中，我们在以下方面检查催化信息学：（1）ML 辅助催化剂表征，（2）从实验数据中提取知识，（3）预测催化性能和反应网络的构建，以及（4 ) 支持 ML 的大规模 QM 模拟。还提供了对这一快速发展领域当前挑战的展望。