First-principles-based multiscale models are ever more successful in addressing the wide range of length and time scales over which material–function relationships evolve in heterogeneous catalysis. They provide invaluable mechanistic insight and allow screening of vast materials spaces for promising new catalysts — in silico and at predictive quality. Here, we briefly review methodological cornerstones of existing approaches and highlight successes and ongoing developments. The biggest challenge is to overcome presently largely static couplings between the descriptions at the various scales to adequately treat the dynamic and adaptive nature of working catalysts. On the road towards a higher structural, mechanistic and environmental complexity, it is, in particular, the fusion with machine learning methodology that promises rapid advances in the years to come.

基于第一原理的多尺度模型在解决各种长度和时间尺度上都取得了更大的成功，这些尺度在物质和功能之间的关系在非均相催化中得以发展。它们提供了宝贵的机械洞察力，并允许筛选大量材料空间，以筛选有希望的新型催化剂-硅胶和可预测的质量。在这里，我们简要回顾了现有方法的方法论基础，并重点介绍了成功和持续发展的过程。最大的挑战是克服目前在各种规模的描述之间的很大程度上静态的耦合，以充分处理工作催化剂的动态和适应性。在走向更高的结构，机械和环境复杂性的道路上，尤其是