OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion

Organometallic complexes are ubiquitous in numerous technological applications, and in particular in homogeneous catalysis. Optimization of such complexes for specific applications is challenging due to the large variety of possible metal-ligand combinations and ligand-ligand interactions. Here we present OM-Diff, an inverse-design framework based on a diffusion generative model for in silico design of such complexes. Due to the importance of the spatial structure of a catalyst, the model operates on all-atom (including H) representations in 3D space. To handle the symmetries inherent to that data representation, OM-Diff combines an equivariant diffusion model with an equivariant property predictor. The diffusion model generates ligands conditioned on a specified metal-center, while the property predictor guides the generation towards novel complexes with desired properties. We demonstrate the potential of OM-Diff by designing optimized catalysts for a family of cross-coupling reactions, and validating a selection of novel proposed compounds with DFT calculations.