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

ABSTRACT: 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 em

SUBMITTER: Yang J 

PROVIDER: S-EPMC12814173 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature