Heterogeneous catalyst design by generative models

Abstract

Understanding interactions between reactive species and surfaces remains a fundamental challenge in materials science and heterogeneous catalysis. Central to this challenge is the efficient and accurate generation of realistic surfaces and intermediate structures. Despite growing efforts, a universal and systematic approach to surface structure generation is still lacking, particularly for complex interfaces. Existing automated protocols often require extensive computations to identify stable configurations. Recent advances in dataset availability and machine learning techniques, especially in generative models, are beginning to show promise for tasks such as catalyst structure generation. In this perspective, we highlight the emerging capabilities of generative models in catalytic research and outline future directions for their applications. These include property-guided surface structure generation, efficient sampling of adsorption geometries, and the generation of complex transition-state structures. We aim to provide catalysis researchers with a clear view of current progress, outline key challenges, and identify opportunities for integrating generative models into the design and discovery of heterogeneous catalysts.

Keywords

Generative model / machine learning / heterogeneous catalyst / surface science

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