CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching

Discovering heterogeneous catalysts tailored for specific reaction intermediates remains a fundamental bottleneck in materials science. While traditional trial-and-error methods and recent generative models have shown promise, they struggle to capture the intrinsic coupling between surface geometry and adsorbate interactions. To address this limitation, we propose CATFLOW, a flow matching-based framework for de novo design and structure prediction of heterogeneous catalysts. Our model operates on a primitive cell-based factorized representation of the slabadsorbate complex, reducing the number of learnable variables by an average of 9.2× while explicitly encoding the surface orientation of the slabadsorbate interface. Experiments on the Open Catalyst 2020 dataset demonstrate that CATFLOW significantly improves the structural fidelity of generated catalysts compared to autoregressive and sequential baselines. Further experiments show that the generated structures accurately capture the adsorption energy distributions of physically plausible interfaces and lie closer to thermodynamic local minima. Recent machine learning approaches have sought to accelerate portions of this workflow, yet each targets only a subset