Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

迄今为止，机器学习在多相催化中的大多数应用都使用黑盒模型来预测与催化性能（即活性或稳定性）相关的可计算物理性质（描述符），例如吸附或形成能。从这些黑盒模型中提取有意义的物理见解已被证明具有挑战性，因为这些黑盒模型的内部逻辑由于其高度复杂性而不易解释。可解释的机器学习方法将黑盒模型的预测能力与基于物理的模型的物理可解释性相结合，提供了黑盒模型的替代方案。在这个观点中，我们讨论了催化研究人员可用的各种可解释的机器学习方法，