过去的十年间，机器学习（ML）在科学研究中得到了广泛应用。本文介绍了机器学习的基本组成部分，包括数据库、特征和算法，并着重介绍了机器学习在化学领域取得的一些重要成就。首先我们介绍了一些化学方面最流行的数据库，这些数据库来自实验或理论模拟，收录了有关小分子或固体材料的各种数据。其次简要介绍了部分重要的表示小分子和固体材料化学环境的二维和三维特征。本文对决策树和深度学习神经网络算法进行了综述，重点介绍了它们的框架和典型应用场景。随后，我们讨论了机器学习在化学中的三个重要应用领域：①逆合成分析，通过机器学习预测有机物的合成途径；②原子模拟，利用机器学习势函数加速势能面采样；③多相催化，使用机器学习辅助催化设计中从合成条件优化到反应机理探索的各个方面。最后我们对机器学习在化学中的应用前景进行了展望。

The past decade has seen a sharp increase in machine learning (ML) applications in scientific research. This review introduces the basic constituents of ML, including databases, features, and algorithms, and highlights a few important achievements in chemistry that have been aided by ML techniques. The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations. Important two-dimensional (2D) and three-dimensional (3D) features representing the chemical environment of molecules and solids are briefly introduced. Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios. Three important fields of ML in chemistry are discussed: ① retrosynthesis, in which ML predicts the likely routes of organic synthesis; ② atomic simulations, which utilize the ML potential to accelerate potential energy surface sampling; and ③ heterogeneous catalysis, in which ML assists in various aspects of catalytic design, ranging from synthetic condition optimization to reaction mechanism exploration. Finally, a prospect on future ML applications is provided.