Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review

Reinforcement learning (RL) and latent world models are emerging as promising tools for modeling complex atomic level changes in catalyst surfaces under real reaction environments. This newly published review in AI Mater introduces a conceptual framework to reconstruct and optimize catalyst surfaces, such as AgPd nanoalloys, using AI-driven techniques. The work presents advancements in using Dreamer based architectures and multi-objective optimization strategies to address long standing challenges in catalytic surface modeling, offering new insights for computational catalysis and smart materials design.

Catalyst surfaces are rarely static under operating conditions, they undergo dynamic transformations that influence their activity, selectivity, and stability. These changes, driven by thermal, electrochemical, and chemical factors, pose major challenges to conventional computational models that often rely on fixed structures or assume ideal conditions.

To address these limitations, Aisha Samreen, a PhD scholar at the School of Materials Science and Engineering, Northwestern Polytechnical University, along with her co-authors, has authored a comprehensive review on the use of reinforcement learning world models in catalyst surface reconstruction. The article, recently published in AI Mater, presents a forward looking perspective on how AI-guided frameworks can help simulate, understand, and control atomic rearrangements in complex catalytic systems.

“Catalyst design requires moving beyond static simulations,” says Aisha Samreen. “World models powered by reinforcement learning allow us to visualize and optimize atomic configurations in response to real world stimuli, such as changing temperatures or adsorbate interactions.”

The review details how latent world models particularly those based on the DreamerV3 framework can capture temporal dynamics and generate future trajectories of catalyst surfaces in a computationally efficient manner. This reduces the reliance on traditional density functional theory (DFT) calculations, which are often time consuming and limited in scalability. By training AI agents within a learned latent space, the approach enables predictive reconstruction of surfaces like AgPd, considering both energy stability and material constraints.

A significant focus of the review is the challenge of generalizing RL models across different catalytic systems. To overcome limitations in transferability and data efficiency, the authors discuss cutting edge approaches such as neuro-symbolic models, uncertainty-aware learning, and closed-loop experimentation, where RL guided models interact with automated lab systems to validate predictions in real time.

While the application of reinforcement learning in catalysis is still evolving, the review makes clear that world models have the potential to transform how scientists explore and design next generation catalysts. “This is not just about modeling,” Aisha adds, “it's about enabling smarter, faster, and more adaptable catalyst development.”

This paper, titled “Reinforcement Learning World Models for Catalyst Surface Reconstruction: State-of-the-Art Review,” has been published in AI Mater.

Samreen A, Azim M, Chen F. Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review. AI Mater. 2025(2):0011, https://doi.org/10.55092/aimat20250011