Machine learning (ML) promises to increase the efficiency of screening a large number of materials for catalytic reactions. However, most existing ML models can only be applied to a specific reaction; therefore, new models usually have to be built from scratch for a new application. The effort and expense needed to create large datasets is also a major drawback of many ML methods. Hence, developing ML models that can be broadly applied to a wide range of different materials and reactions is crucial to further increase efficiency. In this review, we discuss recently developed ML methods in the field of heterogeneous catalysis that represent progress towards more general models. Notable progress has been made in improving generality which can lead to significant increases in efficiency and convenience.

机器学习 (ML) 有望提高筛选大量催化反应材料的效率。但是，大多数现有的 ML 模型只能应用于特定的反应；因此，通常必须为新应用程序从头开始构建新模型。创建大型数据集所需的努力和费用也是许多 ML 方法的主要缺点。因此，开发可广泛应用于各种不同材料和反应的 ML 模型对于进一步提高效率至关重要。在这篇综述中，我们讨论了最近在多相催化领域开发的 ML 方法，这些方法代表了朝着更通用模型的进展。在提高通用性方面取得了显着进展，这可以显着提高效率和便利性。