Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep
learning have
played increasingly prominent roles in materials design and discovery.
Among these, generative AI models, known for their ability to create
unique and complex structures, have emerged as state-of-the-art tools
for materials screening due to their high efficiency and low computational
cost. In catalysis, one of the major challenges is identifying promising
material candidates within an immense chemical space. This challenge
can be addressed using generative approaches, such as diffusion-based
inverse design models. In this study, we present a machine learning-guided
workflow that employed a diffusion model for the inverse design of
bimetallic alloy catalysts for low-carbon ammonia decomposition, a
key reaction for ammonia emission control and sustainable hydrogen
production. Catalyst candidates were evaluated using nitrogen adsorption
energy as the key descriptor, inspired by multiscale modeling. The
proposed workflow identified low-cost, environmentally friendly catalysts
with excellent catalytic performance, which have been validated theoretically
and experimentally. Our framework decoupled the generative and property-prediction
components, enhancing both flexibility and accuracy in the catalytic
material design process.