CompGen: A Conditional Generation Framework for Inverse Composition Design of Catalytic Surfaces

Generating adsorption configurations, that is, how small atoms or molecules bind to complex catalyst surfaces, remains underexplored in inverse materials design. We present CompGen, a conditional generative framework that reformulates 3D structure prediction as a 2D shell-wise composition task centered on the adsorption site. CompGen uses a Chemically Informed Autoencoder (CIAE) to embed sparse compositions into a continuous, periodic table aware latent space learned with a multi-stage pretraining process. A conditional diffusion model then samples in this latent space under multi-physical conditions, including adsorbate identity, adsorption energy, and relevant elements, enabling inverse composition design of catalytic surfaces. Pretrained on a subset of Open Catalyst 2020, CompGen is fine-tuned to more complex high-entropy alloy (HEA) surfaces and achieves strong fine-tuned performance. Extensive experiments show robust zero-shot and few-shot behavior, highlighting CompGen’s effectiveness for data-efficient, domain-transferable inverse design of catalytic surfaces.